A comparison between a photographic shade analysis system and conventional visual shade matching method

Transcription

1 University of Iowa Iowa Research Online Theses and Dissertations Summer 2015 A comparison between a photographic shade analysis system and conventional visual shade matching method Tuo Sheng Joel Khoo University of Iowa Copyright 2015 Tuo Sheng Joel Khoo This thesis is available at Iowa Research Online: Recommended Citation Khoo, Tuo Sheng Joel. "A comparison between a photographic shade analysis system and conventional visual shade matching method." MS (Master of Science) thesis, University of Iowa, Follow this and additional works at: Part of the Oral Biology and Oral Pathology Commons

2 A COMPARISON BETWEEN A PHOTOGRAPHIC SHADE ANALYSIS SYSTEM AND CONVENTIONAL VISUAL SHADE MATCHING METHOD by Tuo Sheng Joel Khoo A thesis submitted in partial fulfillment of the requirements for the Master of Science degree in Oral Science in the Graduate College of The University of Iowa August 2015 Thesis Supervisor: Associate Professor Peter S. Lund

3 Copyright by TUO SHENG JOEL KHOO 2015 All Rights Reserved

4 Graduate College The University of Iowa Iowa City, Iowa CERTIFICATE OF APPROVAL MASTER'S THESIS This is to certify that the Master's thesis of Tuo Sheng Joel Khoo has been approved by the Examining Committee for the thesis requirement for the Master of Science degree in Oral Science at the August 2015 graduation. Thesis Committee: Peter S. Lund, Thesis Supervisor Steven Aquilino Marcos Vargas Fang Qian

5 ACKNOWLEDGMENTS I would like to express my special appreciation and thanks to my advisor, Dr Peter Lund, for you support and guidance to me. I would like to thank you for believing in me and encouraging me when I was doubtful if I should finish the thesis. You chose to invest a tremendous amount of time and energy into guiding me through the whole journey. I would also like to thank my committee members, Dr Steven Aquilino, Dr Marcos Vargas and Dr Fang Qian for serving as my committee members from the start in 2008 through to the final thesis defense in Each one of you have been so encouranging and supportive. I also want to thank you for an enjoyable defense, and for your insightful comments and thought-provoking questions. I would like to thank the American Academy of Fixed Prosthondontics for their financial support through the Stanley D Tylman Research Grant. I would also like to thank Dr Loreen Herwardt for going out of her way to guide me in academic writing. Your kindness will always be remembered. A special thanks to my family. Words cannot express how grateful I am to my mother and father for all your support. Your prayers for me sustained me thus far. I would like to express my deepest appreciation to my lovely wife, Di, for your encouragement and support, and sacrifices you have made on my behalf. I could not have done it without you. Above all, I would like to thank God for granting me the grace to complete this thesis. Through the difficult times, You have been with me and have given me the strength to persevere. Every step on this journey has been ordained by You and I am grateful because of this. ii

6 ABSTRACT There are no previous studies validating the accuracy and repeatability of ClearMatch photographic shade analysis system. The purpose of this study was to compare the shade matches performed by a photographic shade analysis system (ClearMatch) with conventional visual shade matching method under simulated clinical conditions. Three shade matching methods were used to match twelve shade tabs under simulated clinical conditions using a Vita Classical shade guide: conventional visual shade matching using 3 human raters (VM Visual method), photographic shade analysis system (CM - ClearMatch) using two different ways of normalizing the image (CM-A2 and CM-ref respectively). Shade matching for all methods was completed at two separate sessions. The Kappa statistic was used to determine the intra-rater and interrater agreement. CIELAB values of the shade results were used to produce scatter plots as well as to calculate the color difference (delta E) between VM and CM groups. There was no inter-rater agreement between VM and CM-A2 (k=0.000 and k=0.015 for the first and second sessions respectively) while VM and CM-ref showed weak agreement (k=0.244 and k=0.091 respectively). Intra-rater agreement was strong in all groups VM, CM-A2 and CM-ref (0.705, and respectively). CM-A2 had 2 (8.3%) shade matches with a delta E of less than 2.6 (clinically imperceptible), while CM-ref had 12 (50.0%) imperceptible matches. CM-A2 had an additional 16 (66.6%) shade matches with delta E of less than 5.5 (clinically acceptable), while CM-ref had 23 (95.8%) additional acceptable matches. There was poor agreement in exact shade matches between conventional visual shade matching method and the photographic shade analysis system. The repeatability of the photographic shade analysis system was shown to be comparable to conventional visual shade matching. Using conventional shade matching as the gold standard, the iii

7 capability of this photographic shade analysis system to accurately shade match has not been achieved. iv

8 PUBLIC ABTRACT There are no previous studies validating the accuracy and repeatability of ClearMatch photographic shade analysis system. The purpose of this study was to compare the shade matches performed by a photographic shade analysis system (ClearMatch) with conventional visual shade matching method under simulated clinical conditions. Three shade matching methods were used to match twelve shade tabs under simulated clinical conditions using a Vita Classical shade guide: conventional visual shade matching using 3 human raters (VM Visual method), photographic shade analysis system (CM - ClearMatch) using two different ways of normalizing the image (CM-A2 and CM-ref respectively). Shade matching for all methods was completed at two separate sessions. The Kappa statistic was used to determine the intra-rater and interrater agreement. CIELAB values of the shade results were used to produce scatter plots as well as to calculate the color difference (delta E) between VM and CM groups. There was poor agreement in exact shade matches between conventional visual shade matching method and the photographic shade analysis system. The repeatability of the photographic shade analysis system was shown to be comparable to conventional visual shade matching. Using conventional shade matching as the gold standard, the capability of this photographic shade analysis system to accurately shade match has not been achieved. v

9 TABLE OF CONTENTS LIST OF TABLES... viii LIST OF FIGURES...x CHAPTER 1 INTRODUCTION... 1 Research question and purpose of this study... 3 Research hypotheses... 3 CHAPTER 2 LITERATURE REVIEW...7 Introduction...7 The color stimulus...7 Tripartite nature of color...7 Light source...7 Spectral power distribution...8 Color temperature and CRI...8 Object...9 Spectral reflectance...9 Transmittance and refraction...9 Absorption, scattering and translucency...9 Opalescence...10 Fluorescence...10 Surface texture...10 Observer...11 Metamerism...11 Color systems used in dentistry...12 Munsell Color Order System...12 Advantages, disadvantages and application...13 CIELAB Color Space...14 Description of CIELAB color space...14 Advantages, disadvantages and application...15 CIE LCH Color System...15 RGB color system...16 Perceptibility and acceptability...16 Laboratory thresholds for color difference detection...17 Clinical thresholds for color difference detection...20 Color of human teeth...21 Tooth color measurements...21 Tooth color differences between genders...23 Tooth color change with aging...24 Tooth color differences from tooth to tooth...24 Color differences between different locations on a single tooth...25 Population differences...27 Visual shade matching...28 Basic concepts...28 Light sources...28 Shade matching environment...29 Shade guides...29 VITA Classical guide...30 Problems with VITA Classical shade guide...31 Limited range compared to human teeth...31 vi

10 Organization of the shade guide...33 Manufacturer variation and construction methods / materials...34 VITA 3D Master guide...35 Limitations in visual shade matching...37 Dehydration of teeth...37 Training and experience of the person taking a shade match...38 Metamerism...41 Color vision deficiencies...42 Gender differences...44 Shade matching instruments...46 Colorimeters...46 Spectrophotometers...50 Photographic...57 Theory...57 Description of a photographic-based shade matching system...61 CHAPTER 3 MATERIALS AND METHODS...66 General description of the experiment...66 Comparing performance of VITA EasyShade and SpectroShade Micro...66 Assembly of a representative VITA Classical shade guide for visual matching...67 Selection and preparation of target shade tabs...68 The clinical shade matching environment...69 Visual shade matching...70 The camera set up...71 ClearMatch data acquisition and analysis...72 Statistical and Descriptive Analysis...73 CHAPTER 4 RESULTS...91 Shade matching results...91 Inter-observer agreement...91 Intra-observer agreement...92 Color differences between shades chosen using conventional visual shade matching and ClearMatch methods...93 Correlation between visual matching and ClearMatch...94 CHAPTER 5 DISCUSSION CHAPTER 6 CONCLUSION BIBLIOGRAPHY vii

11 LIST OF TABLES Table 2-1 Laboratory and clinical studies on color acceptability and perceptibility thresholds...64 Table 2-2 Clinical studies that measured tooth color using colorimeters and spectrophotometers...65 Table 3-1 CIE L*, a* and b* values for VITA Classical shade guide tabs from measurements of an arbitrarily chosen guide using two intraoral spectrophotometers (VITA EasyShade and SpectroShade Micro), and also reported in two previous research studies. (Bayindir, et al. 2007; Paravina, et al. 2007) Table 3-2 Color differences between measurements of shade tabs from an arbitrarily chosen shade guide (VITA Classical) made by two intraoral spectrophotometers (VITA EasyShade and SpectroShade Micro), and measurements reported in two previous studies (Bayindir, et al. 2007; Paravina, et al. 2007) Table 3-3 Repeated measurements of CIE L*, a* and b*from the middle third of four arbitrarily selected Trublend SLM shade tabs using an intraoral spectrophotometer (SpectroShade Micro) Table 3-4 CIE L*, a* and b* coordinates of all 16 shade tabs from ten arbitrarily chosen VITA Classical shade guides, and the overall color differences (ΔE) from the mean coordinates for each shade. Color measurements were made of the middle third of the shade tabs using an intraoral spectrophotometer (SpectroShade Micro) (Continues to next page) Table 3-5 CIE L*, CIE a* and CIE b* values of the representative VITA Classical shade guide used for visual matching Table 3-6 CIE L*, a* and b* values for one arbitrarily chosen Trublend SLM, Bioform IPN and Portrait IPN shade guide, from a single measurement of the middle third of each shade tab using an intraoral spectrophotometer (SpectroShade Micro) Table 3-7 Twelve arbitrarily selected target shade tabs and CIE color differences from the VITA Classical shades that were spatially closest on the scatter plots Table 3-8 Target shade tabs with their corresponding assigned random numbers Table 3-9 Repeatability of the camera and flash equipment: ClearMatch shade from 10 consecutive digital photographs of a Portrait D3 shade tab taken at one minute intervals using the experimental setup Table 3-10 Repeatability of reconstructing the experimental set up: ClearMatch shade from 10 consecutive digital photographs of a Portrait D3 shade tab taken after disassembly and reassembly of the camera tripod and dentoform setup viii

12 Table 4-1 Shade matching results using conventional shade matching and ClearMatch photographic methods...95 Table 4-2 Kappa statistics for inter observer agreement...96 Table 4-3 Kappa statistics for intra observer agreement...97 Table 4-4 Color differences between shades determined using conventional visual shade matching and the ClearMatch photographic methods Table 4-5 Results of Spearman's rank correlation tests for CIE L*, a* and b* between shades chosen using conventional visual shade matching and ClearMatch photographic methods ix

13 LIST OF FIGURES Figure 3-A Scatter plot of shade tabs from Trublend SLM, Bioform IPN, Portrait IPN, and VITA Classical shade guide, with shades selected for the target tabs indicated Figure 3-B Scatter plot of shade tabs from Trublend SLM, Bioform IPN, Portrait IPN, and VITA Classical shade guides, with shades selected for the target shade tabs indicated Figure 3-C Dentoform and camera set up for visual shade matching and recording of the digital photographs...89 Figure 3-D Computer screen capture of the digital photograph and shade map in the ClearMatch computer program Figure 4-A Scatter plot of rank of VM against rank of CM-A2 for CIE L* coordinates for session Figure 4-B Scatter plot of rank of VM against rank of CM-A2 for CIE L* coordinates for session Figure 4-C Scatter plot of rank of VM against rank of CM-ref for CIE L* coordinates for session Figure 4-D Scatter plot of rank of VM against rank of CM-ref for CIE L* coordinates for session Figure 4-E Scatter plot of rank of VM against rank of CM-A2 for CIE a* coordinates for session Figure 4-F Scatter plot of rank of VM against rank of CM-A2 for CIE a* coordinates for session Figure 4-G Scatter plot of rank of VM against rank of CM-ref for CIE a* coordinates for session Figure 4-H Scatter plot of rank of VM against rank of CM-ref for CIE a* coordinates for session Figure 4-I Scatter plot of rank of VM against rank of CM-A2 for CIE b* coordinates for session Figure 4-J Scatter plot of rank of VM against rank of CM-A2 for CIE b* coordinates for session Figure 4-K Scatter plot of rank of VM against rank of CM-ref for CIE b* coordinates for session Figure 4-L Scatter plot of rank of VM against rank of CM-ref for CIE b* coordinates for session x

14 1 CHAPTER 1 INTRODUCTION Dentists face many challenges in reliably matching tooth shades (Douglas and Brewer 2003; Omar, Atta, and El-Mowafy 2008). First, previous studies demonstrate that visual shade matching is subjective (Douglas, Steinhauer, and Wee 2007) and the results vary among observers and also within an individual observer (Lindsey and Wee 2007). Second, the light source influences shade matching because the spectral composition of the light reflecting off an object affects the perceived color as a result of metamerism (Corcodel et al. 2009). Third, shade guides are not uniformly distributed in the CIELAB color space and do not cover the entire range of natural tooth shades (Bayindir et al. 2007). Finally, enamel translucency and the polychromatic nature of dentin interact to produce depth of shade that is difficult to characterize (Hasegawa, Ikeda, and Kawaguchi 2000; Ardu et al. 2010). Colorimeters and spectrophotometers have been used to overcome some of those problems. However, the results of shade matching studies with these instruments have varied with respect to accuracy and repeatability (Hugo, Witzel, and Klaiber 2005; Dozic et al. 2007; Dasilva et al. 2008; Karamouzos et al. 2007). These proprietary systems are generally expensive because research and development are costly (Brewer, Wee, and Seghi 2004). Consequently, few dentists use these systems. As an alternative to colorimetric or spectrophotometric methods, photographicbased shade matching has been proposed. Digital imaging software (Adobe Photoshop) has been used to analyze camera-based digital images on a computer monitor (Caglar et al. 2009; Jarad, Albadri, and Mair 2008; Jarad, Russell, and Moss 2005). Using this method, there is an improvement in shade matching performance over visual methods, however there are several disadvantages. A human observer is still required to visually compare the image of a tooth with a standard. In addition, camera-based digital images

15 2 require color calibration to correct for different exposure variables and camera processing hardware using known standards (Bister 2006; Bengel 2003) or computer algorithms (Wee et al. 2006; Hong, Luo, and Rhodes 2001). Calibration is specific to each camera set up and, thus, cannot be used to analyze results produced with other cameras. ClearMatch (Clarity Dental, Salt Lake City, UT), a photographic-based shade mapping software system, was introduced in To use this technology, the dentist needs a digital camera, the manufacturer s white and black reference color tabs and a tooth shade guide. Developers claim that this software can calibrate and color correct a digital image to match the original tooth shade, regardless of the type of digital camera used. This system has several advantages. First, the cost is significantly lower than the cost of purchasing a colorimeter or spectrophotometer. Second, the database includes a wide selection of commercially available shade guides that can be used to match the shade. Third, the dentist can send the digital images by electronic mail to the dental laboratory for shade analysis and the laboratory can send the shade back to the dentist to confirm the shade. Finally, the software can produce simple or detailed shade maps, which, along with the digital images, provide the dental technician more color information than the basic shade tab designation. Since this technology was introduced in 2001, investigators have not independently assessed its accuracy and validity. If this system can accurately and reliably match tooth color, it would be a cost-effective alternative to the other shade matching systems and it could circumvent the problems associated with human color perception (Capa et al. 2010). Research question and purpose of this study The research question for this study was whether the clinical shade matching performance of the ClearMatch photographic shade analysis system was comparable to conventional visual shade matching. The purpose of this study was to compare the shade

16 3 matches determined by the ClearMatch system with conventional visual shade matching method under simulated clinical conditions. Research hypotheses Null hypothesis (1): There is no agreement in the shades determined by ClearMatch photographic shade analysis system using VITA A2 as the shade reference and shades determined by conventional visual shade matching in session one. Alternate hypothesis (1): There is an agreement in the shades determined by ClearMatch photographic shade analysis system using VITA A2 as the shade reference and shades determined by conventional visual shade matching in session one. Null hypothesis (2): There is no agreement in the shades determined by ClearMatch photographic shade analysis system using VITA A2 as the shade reference and shades determined by conventional visual shade matching in session two. Alternate hypothesis (2): There is an agreement in the shades determined by ClearMatch photographic shade analysis system using VITA A2 as the shade reference and shades determined by conventional visual shade matching in session two. Null hypothesis (3):

17 4 There is no agreement in the shades determined by ClearMatch photographic shade analysis system using the visually selected shade as the shade reference and shades determined by conventional visual shade matching in session one. Alternate hypothesis (3): There is an agreement in the shades determined by ClearMatch photographic shade analysis system using visually selected shade as the shade reference and shades determined by conventional visual shade matching in session one. Null hypothesis (4): There is no agreement in the shades determined by ClearMatch photographic shade analysis system using the visually selected shade as the shade reference and shades determined by conventional visual shade matching in session two. Alternate hypothesis (4): There is an agreement in the shades determined by ClearMatch photographic shade analysis system using visually selected shade as the shade reference and shades determined by conventional visual shade matching in session two. Null hypothesis (5): There is no agreement in the shades determined by ClearMatch photographic shade analysis system using VITA Classical shade A2 as the reference shade and ClearMatch photographic shade analysis system using the visually determined shade as the reference shade in session one. Alternative hypothesis (5)

18 5 There is an agreement in the shades determined by ClearMatch photographic shade analysis system using VITA Classical shade A2 as the reference shade and ClearMatch photographic shade analysis system using the visually determined shade as the reference shade in session one. Null hypothesis 6): There is no agreement with visually determined shades between ClearMatch photographic shade analysis system using VITA Classical shade A2 as the reference shade and ClearMatch photographic shade analysis system using the visually determined shade as the reference shade in session two. Alternative hypothesis (6) There is an agreement with visually determined shades between ClearMatch photographic shade analysis system using VITA Classical shade A2 as the reference shade and ClearMatch photographic shade analysis system using the visually determined shade as the reference shade in session two. Null hypothesis (7): There is no agreement in the repeatability of shades determined by ClearMatch photographic shade analysis system or in the repeatability of shades determined by conventional visual shade matching. Alternative hypothesis (7): There is an agreement in the repeatability of shades determined by ClearMatch photographic shade analysis system or in the repeatability of shades determined by conventional visual shade matching.

19 6 Null hypothesis (8): There is no correlation between CIELAB color coordinates for shades determined by ClearMatch photographic shade analysis system and shades determined by conventional visual shade matching. Alternative hypothesis (8): There is correlation between CIELAB color coordinates for shades determined by ClearMatch photographic shade analysis system and shades determined by conventional visual shade matching.

20 7 CHAPTER 2 LITERATURE REVIEW Introduction Successful replication of the natural appearance of teeth in a dental prosthesis requires accurate rendition of tooth contour, embrasure form, position, inclination, surface texture and tooth color. While the relative importance of these factors has been debated, achievement of an accurate color match is often difficult to achieve. Tooth shade matching is subjective and challenging because color is not easily quantifiable. Unlike standard color specimens that are flat, opaque and of a single color, human teeth have non-planar surfaces and are non-homogenous in their structure, color and translucency. The color stimulus Tripartite nature of color Color is the visual perceptual sensation of light that defines the appearance of our surroundings. To determine the color of an object, one needs to consider the nature of the light source that illuminates an object, the spectral reflectance properties of the object being viewed, and the nature of human color perception (Berns 2000). In other words, variation in any of these three aspects may affect the visual perception of color. Light source Light is the part of the electromagnetic spectrum visible to the human eye. The visible light range includes wavelengths from 380 to 760 nanometers (CIE 1987). Light photons with shorter wavelengths (400 nm) appear blue and those with longer wavelengths (700 nm) appear red.

21 8 Spectral power distribution A light source can be described in terms of its relative power emitted at each wavelength in the visible spectrum. The spectral power distribution is derived by plotting this power as a function of the wavelength (Berns 2000). Every light source has a unique spectral power distribution, which results in a characteristic hue due to the relative power of light at each wavelength. Color temperature and CRI One can characterize the hue of a light source by matching it with the equivalent hue of light emitted by a black body radiator at a specific temperature (Wyszecki 1982). In physics, a black body radiator is an idealized object that absorbs all electromagnetic radiation falling on it and it appears black when cold. When the black body heats up, it re-emits radiation in a characteristic spectrum of light that is temperature dependent. The temperature is stated in units of absolute temperature, Kelvin (K). Lower color temperatures (2,700 to 3,000 K) are considered warm colors (red to yellow) while higher color temperatures (5,000 K or more) are cool colors (blue to white). Some sources of light, such as fluorescent lamps, do not emit light by thermal radiation. They are nevertheless assigned color temperatures that visually match the black body radiation spectrum. This color temperature is termed the correlated color temperature. A color rendering index (CRI) is used to measure the quality of a light source at a specific color temperature. The CRI ranges from 0 to 100 and a higher number denotes a light source closer in spectral characteristics to the reference standard light source for a given color temperature. An example of a reference light source for 5,500K is the color temperature of the sky on a clear noon day.

22 9 Object Spectral reflectance Light from a light source that falls on an object can be absorbed, reflected, scattered, diffracted, transmitted or a combination of these. As a result, the light reflected from the object has a different spectral power distribution from the incident light, giving the object its characteristic color (Preston 1980). The proportion of visible light that is reflected off an object at each wavelength interval is known as the spectral reflectance factor for that interval, which ranges from 0 to 1 (Westland 2003). The spectral reflectance factors in the visible light range combine to form a spectral reflectance curve, analogous to the spectral power distribution of the light source. Transmittance and refraction When light passes through a transparent object unchanged, a small portion of the light is reflected while the remainder of the light is refracted at the boundary and transmitted through the object. Refraction is the change in the direction of a light beam caused by the slowing down of light at the boundary. The refractive index of a transparent object is therefore equal to the speed of light in air divided by the speed of light in this object. A higher refractive index causes more light to be reflected and a higher degree of light refraction (Berns 2000). Absorption, scattering and translucency Light that falls on an object is transmitted, refracted, scattered, and/or absorbed to varying degrees. If most of the light is transmitted through the object unaltered, the object appears transparent. If most of the light is reflected or absorbed, the object appears opaque. If particles in an object cause some of the light to scatter while the rest of the light is able to transmit, the object appears translucent. The degree of translucency of an object is dependent on the number of particles, the size of particles and the refractive

23 10 index of the particles. The greater the number, size and refractive index of the particles, the higher the incidence of light scattering, resulting in less translucency (Berns 2000). A translucent object allows light to be transmitted through, causing the appearance of the object to be influenced by the color of the background. Enamel exhibits more translucency than dentin, allowing the underlying dentin or the back of the oral cavity to influence its final color. Opalescence Opalescence is the phenomenon where longer wavelengths of light entering an object are scattered by highly refractive particles dispersed within the object, while the shorter wavelengths pass through unaltered. This gives the object a more bluish appearance when viewed away from the direction of the passing light and a more orange red appearance when viewed close to the path of the light (Craig s Restorative Dental Materials 13 th edition). One can see this light blue-gray reflected hue in the incisal third of some maxillary incisors. Fluorescence Fluorescent objects such as teeth absorb energy at shorter wavelengths that are that are outside the visible spectrum and they re-emit energy at a longer wavelength in the visible light spectrum. This effect is similar to that seen when ultraviolet light, which is invisible to the eye, is absorbed in white cloth and re-emitted as visible light. Surface texture An object may have a surface texture that is glossy or matt. Light reflection from the surface of an object is termed specular reflection. Under a zero degree observer and 45 degree illumination set up, a glossy surface tends to exhibit a lower value and higher chroma than a matt surface of the same color. This is because a large proportion of the incident light is reflected off the glossy surface at an equal an opposite direction (45

24 11 degrees), and is not in the path of the observer. On the other hand, the incident light falling onto a matt surface is scattered, resulting in some of the scattered light being coincident with the 0 degree path to the observer. Observer For a human observer, the lens of the eye focuses light entering the eye onto the retina s rods and cones, which are specialized photo-sensitive cells. Rods can sense very low levels of light and therefore facilitate vision at low levels of illumination. However they only provide neural information about the achromatic aspect for visual perception. On the other hand, cones provide the chromatic color information, but only at high levels of light and therefore facilitate color vision at high levels of illumination. Therefore, at low levels of light, one cannot discern colors because the cones are inactive. Different types of cones vary in their sensitivity to different light wavelengths, with peak sensitivities of 420 nm for short cones, 530 nm for medium cones, and 560 nm for long cones. The relative responses of the three types of cones generate opponent-type color signals in three parameters: luminance, red-green, and yellow-blue, resulting in what is called trichromacy. The luminance signal results from the addition of signals from the long and medium cones. The red-green signal results from the signals of the medium cones being subtracted from the signals of the long cones. The yellow-blue signal results from the addition of long and medium cones signals and subtraction of the short cone signals. These signals travel via the optic nerve through the lateral geniculate nucleus in the central brain to the visual cortex at the back of the brain. The visual cortex combines color information with spatial and temporal information to create a unified visual perception (Westland 2003; Barna et al. 1981). Metamerism The perceived color of an object is dependent upon the light source that is illuminating it and the observer that is viewing it. If the spectral power distribution of the

25 12 light source changes, or the observer or viewing conditions change, the final color stimulus will change, even as the object properties remain unchanged (Berns 2000). Illuminant metamerism is the visual phenomenon in which objects with different spectral reflectance distributions have the same color appearance under a given light source, but appear different under another light source having different spectral power distribution (Preston 1980). Geometric metamerism results when the color of two objects matches when viewed in one angle, but fail to match when viewed from another angle. Material attributes such as translucency, surface gloss and surface texture may account for the change in color appearance of an object when viewed from different angles. Observer metamerism occurs between individuals with differences in their color vision. Under identical lighting and viewing conditions, two objects may appear the same color to one observer but appear as a mismatch to another observer. The proportion, quantity and distribution of the different types of cones and the light sensitivity of each type of cone differ between observers, altering the importance of different wavelengths of light to the color perception of each observer (Berns 2000). Color systems used in dentistry Color systems used commonly in dental clinical and research settings are the Munsell color order system, the CIE L*a*b* (CIELAB) and CIE L*c*h* color coordinate systems. The RGB color system is used to describe tooth color in digital images and all visual media devices that record, measure, manipulate or display digital images, such as digital cameras, colorimeters, computers and display monitors respectively. Munsell Color Order System The Munsell color order system defines the color of an object using three independent dimensions: Hue, Value and Chroma (Kuehni 2002). Hue is an attribute of a

26 13 perceived color associated with the predominant wavelengths of light causing the particular color stimulus. Value is an attribute of a perceived color associated with its lightness, ranging from black to white. Chroma is amount of color saturation of a perceived color at a given lightness (Berns 2000). The Munsell color order system has 10 principal Hues, red (R), yellow-red (YR), yellow (Y), green-yellow (GY), green (G), blue-green (BG), blue (B), purple blue (PB), purple (P), and red-purple (RP), arranged clockwise in a circle in spectral order. Each hue is divided further into ten gradients. The pure Hue is given a designation of 5 (e.g. 5R for a pure red Hue). Within a given Hue, designations decrease to 1 going counter-clockwise and increase to 10 going clockwise. For example, 1 red is next to 10 red-purple and 10 red is next to 1 yellow-red. Value is divided into 10 steps in the vertical dimension, in which black is 0 at the bottom, and white is 10 at the top. For a defined Hue and Value, the Chroma scale extends horizontally outward, and increases in Chroma as the scale extends further away from the center of the Hue circle. Unlike Hue and Value notations, the Chroma scale is open-ended and is limited physically by the concentration of the colorants. However, as the Value approaches either ends of the Value scale, the range of Chroma decreases. The combination of Hue, Value and Chroma results in the Munsell color solid, in which each Hue extends to its maximum Chroma at each Value. In a three dimensional view, the Munsell color order system takes the appearance of an asymmetrical globe. A Munsell notation is defined as H V/C, where H represents Hue, V represents Value, and C represents Chroma. For example, 5G 4/3 denotes a color with a Hue of 5 green, a Value of 4, and a Chroma of 3 (Judd and Wyszecki 1975). Advantages, disadvantages and application The Munsell color system was developed based on visual color matching and not spectral properties, an advantage in clinical use because the clinical endpoint is a visually matching shade of a restorative material. Furthermore, any color mismatch can be

27 14 described in terms of Hue, Value and Chroma, attributes that can be understood by a layperson, for example a dentist or laboratory technician. However, a disadvantage is that one cannot use the Munsell color system to quantify color differences based on spectral properties and hence limits the system s use and scope in research. Another disadvantage of using the Munsell color system in dentistry is the difference in appearance of the fabricated color samples to teeth, with the latter being opaque, flat and rectangular, compared with teeth that are translucent and convex in contour. CIELAB Color Space Description of CIELAB color space The Commission Internationale de l Eclairage (CIE) developed the CIE 1976 L*, a*, b* color space, with the official abbreviation CIELAB. The L* coordinate or axis quantifies the lightness and has a scale from zero to 100. The a* axis represents rednessgreenness with zero being achromatic, a negative a* value representing more greenness than redness, and a positive a* value representing more redness than greenness. The b* axis represents yellowness-blueness with zero being achromatic, a negative b* denoting more blueness than yellowness, and a positive b* denoting more yellowness than blueness. Advantages, disadvantages and application CIELAB was developed to create a more perceptually uniform color space that could be correlated with the visual appearance of colors. This color space is currently the most widely used color space in dental color research as the difference in colors can be calculated by measuring the Euclidean distance between two colors and these color differences have been correlated with visual perception. Color difference is represented by the Greek symbol, Δ (delta) and is described according to the following formula, ΔE xy = [(L* x L* y ) 2 + (a* x a* y ) 2 + (b* x b* y ) 2 ] 1/2

28 15 where x and y denote two different colors in the CIELAB color space and ΔE denotes the overall color difference between x and y. Due to the mathematical operations in the formula, total color difference is an absolute value and does not provide any information about the direction of the difference. Information about the direction of color difference is determined by the difference in each of the three CIELAB coordinates: ΔL*, Δa* and Δb* respectively expressed by the following formulas, ΔL* xy = L* x L* y Δa* xy = a* x a* y Δb* xy = b* x b* y where x and y denote two different colors in the CIELAB color space. A disadvantage of CIELAB is that color differences in the a* and b* axes cannot be described in terms of a change of hue and chroma, making it less applicable in the clinical setting. CIE LCH Color System The chroma and hue of any color in the CIELAB color space can be geometrically described according to the following formulas: CIE 1976 a,b (CIELAB) chroma : C ab * = (a* 2 + b* 2 ) 1/2 CIE 1976 a,b (CIELAB) hue : h ab = arctan (b*/a*) The full gamut of colors defined by CIE L*, C ab * and h ab forms the LCH color space, that can be represented as a globe. CIE L* represents lightness and is represented by a vertical axis with a scale of 100, with 0 being black at the bottom and 100 being white at the top, exactly like CIELAB. C ab * represents chroma, which is the horizontal Euclidian distance from the vertical L* axis, with 0 being achromatic and 100 or more being very highly chromatic. h ab represents hue, or hue angle and is the circumferential

29 16 location on circular axes around the vertical L* axis, ranging from 0 (red) through 90 (yellow), 180 (green), 270 (blue) and back to 0. Just like Munsell s color system, the Lch descriptors allow colors in the CIELAB color space to be interpreted in terms of lightness, chromaticity and hue, parameters that are more clinically meaningful and useful. Unfortunately, the scale and intervals used in C ab * and h ab have not been fully characterized based on human visual perception in the way ΔE has been. Thresholds in perceptibility and acceptability to changes in C ab * and h ab in the tooth color range have also not be characterized. As such, a person is able to describe color changes in C ab * and h ab values but is not able to determine how significant the differences are clinically. RGB color system The RGB color model is an additive color model in which the primary colors: red, green and blue are added in different proportions to reproduce a broad range of colors (Hunt 2004). This color model is used primarily in visual media devices such as digital cameras, computer monitors and video projectors, and is a device-dependent color space. Perceptibility and acceptability Perceptibility of a color difference is a judgment based solely on the physiological sensitivity of the human visual system. When measuring perceptibility of a color difference, an observer is not interpreting the importance of the difference but assessing only if a difference exists. In other words, the human observer acts like a color difference detector. In contrast, acceptability of a color change is a judgment of the importance of the color difference in relation to the context of the situation. For example, two tooth color reproductions can be made where one is slightly green and the other slightly red. Based on perceptibility, both may have a color difference of equal magnitude from the standard.

30 17 However, most observers would find the slightly green reproduction less acceptable than the red reproduction (Berns 2000). Dental restorations do not need to have the exact color measurements as the patient s natural teeth to be considered a successful dental shade match because the human eye has both a threshold for perceiving differences in color and a threshold for esthetic acceptability. The perceptibility threshold is established when 50 percent of observers cannot distinguish between the shades of two color specimens and 50 percent of observers are able to identify the two color specimens as different shades. The acceptability threshold is determined when 50 percent of subjects determine that the shade match is esthetically unacceptable and 50 percent of the subjects determine that it is acceptable (Chu, Trushkowsky, and Paravina 2010). Table 2-1 shows laboratory and clinical studies on color acceptability and perceptibility thresholds. Laboratory thresholds for color difference detection Two groups of investigators conducted in vitro studies to investigate perceptibility thresholds. Kuehni and Marcus, using textiles and paint specimens under ideal viewing conditions, found a perceptibility threshold of one ΔE unit (Kuehni and Marcus 1979). This meant that 50% of the observers perceived a color difference of one ΔE unit. This study used 63 observers, with a large proportion of them being experts in the color industry. No significant difference was found between perceptibility and acceptability thresholds. The results of this study have been frequently cited as the standard for perceptibility and acceptability thresholds of small color differences. However, Kuehni and Marcus s study on perceptibility and acceptability thresholds are not directly applicable to a clinical tooth color matching scenario because the study s color specimens and viewing conditions are different from the clinical scenario. A study by Seghi et al investigated the color perceptibility of 27 observers (23 dentists and four dental technicians) using 31 custom fabricated monochromatic porcelain

31 18 disks under controlled viewing conditions (Seghi, Hewlett, and Kim 1989). A series of ten controlled dilutions of pink, yellow, and gray pigmented porcelain disks were fabricated, and one porcelain disk was left un-pigmented to serve as a control. The observers were asked to rank the disks in order of increasing saturation according to their hue groups. The disks were measured with a colorimeter (CR-100 Chroma Meter, Minolta Corp, Ramsey, NJ, USA) and the CIELAB values were recorded. Using specific statistical methods to analyze the rank orders of porcelain disks shade matches, Seghi et al found that all observers could discern a color difference in the porcelain disc of 2.0 ΔE units and greater. Though the perceptibility threshold for 50% of the observers was not reported, the research group did reported that the observers made infrequent incorrect judgments when the color differences were between 1.0 to 2.0 ΔE units but more frequent incorrect judgments when the color differences were less than 1.0 ΔE unit.. The matching of monochromatic porcelain disks under controlled in vitro viewing conditions would allow observers to discern smaller color differences than in a typical clinical tooth color matching scenario. Two other research groups conducted in vitro studies to investigate acceptability thresholds. Ruyter et al had twelve observers (six dentists and six chemists) evaluate the color acceptability of 13 pairs of chemically cured composite (Concise Universal Shade, 3M, MN, USA) discs. The first disc of each pair was used as a control and was kept in the dark, while the second disk of each pair was exposed to a xenon light source for different amounts of time (6-200 hours) to cause color changes in the composite material. The pairs of exposed and unexposed discs were viewed against a white background in a viewing box under artificial daylight (D 65 ). The observers were asked to judge the acceptability of the color match in the context of a color difference between veneers on adjacent teeth. The investigators found that 50% of the observers considered a color difference of 3.3 ΔE units unacceptable (Ruyter et al 1987).

32 19 Ragain and Johnston investigated color acceptability threshold of 48 observers (12 dental auxiliaries and hygienists, 12 dentists, 12 dental material scientists and 12 patients) using different shades of composite resin discs. A standard disc was used to represent tooth color and six other discs of differing shades were used to represent composite resin restorations. The observers were asked to judge the color acceptability between pairs of discs in a viewing booth illuminated with artificial daylight. Ragain and Johnston reported 50% of the observers considered a color difference of 2.29 ΔE units unacceptable (Ragain and Johnson 2000). In both of these acceptability threshold studies, the observers were asked to judge the clinical acceptability of a color match in the context of placing composite restorations on the teeth of human subjects, which would have been abstract in nature, as they were viewing pairs of colored composite discs in a viewing booth instead. Douglas and Brewer evaluated the color perceptibility and acceptability of 20 prosthodontists using 60 commercially fabricated metal ceramic crown specimens in a viewing booth under D 65 lighting conditions. The crowns were fabricated to match the color of VITA Classical A3.5 shade tab. A colorimeter (CR 321 Chroma Meter, Minolta Corp., Ramsey, NJ, USA) was used to measure the color of each of the crowns in CIELAB values. Three groups of 10 crown pairs were assembled based on varying color differences in only one of the three color dimensions (L*, a* or b*) with the other two dimensions being relatively constant. The investigators reported that 50% of the observers considered a color difference of 1.7 ΔE units unacceptable and a color difference of 0.4 ΔE units perceptible. In addition, a subgroup analysis of the results showed that thresholds of acceptability were significantly smaller in metal ceramic crowns differing in their red chroma (mean 1.1 ΔE units) as compared with crowns differing in their yellow chroma (2.1 ΔE units) (p < 0.05) (Douglas and Brewer 1998). In summary, the in vitro studies of color perceptibility and acceptability thresholds showed differing results, due to differences in methodologies, in particular,

33 20 type of observers, and visual specimens. However, there was a trend suggesting that color perceptibility thresholds were generally lower than color acceptability thresholds. Clinical thresholds for color difference detection Several groups have done studies to identify perceptibility and acceptability thresholds in vivo. Johnston and Kao had two observers visually assess the esthetics of 42 composite resin veneers in patients at one week, six months and twelve months after veneer placement. The two observers compared the color of the veneers with a neighboring tooth using the United States Public Health Service (USPHS) criteria, where alpha is a color match and bravo is a mismatch, and the extended visual rating scale for appearance match (EVRSAM), where 0 was designated a perfect match and 10 was designated an unacceptable color match. The investigators used a colorimeter (Chroma Meter CR 121, Minolta Corp., Ramsey, NJ, USA) to obtain the CIELAB values of each veneer and its corresponding control tooth, which was used to calculate the color difference between them. They found a mean color difference of 3.7 ΔE units to be a match and a mean color difference of 6.8 ΔE units to be a mismatch (Johnston and Kao 1989). Though the instrument used for color measurement was not developed for intraoral application and the data had large standard deviations, Johnston s and Kao s findings suggested that the perceptibility and acceptability thresholds under clinical conditions were higher than thresholds determined in vitro. A study by Douglas et al confirmed these findings. These investigators fabricated a denture that allowed ten maxillary left central incisors of different shades relative to the right central incisor to be interchanged on the denture base. Using a spectroradiometer (PR 705, Photo Research Inc., Chatsworth, CA, USA) to measure the color of denture teeth, the investigators selected ten left maxillary central incisors with color differences that ranged from 1ΔE units to 10ΔE units. An edentulous volunteer wore the denture while 28 dentists evaluated the acceptability and perceptibility of the ten left central

34 21 incisors relative to the right central incisor. The perceptibility threshold was 2.6 ΔE units while the acceptability threshold was 5.5 ΔE units (Douglas, Steinhauer, and Wee 2007). Color of human teeth Tooth color measurements Before the use of colorimeters and spectrophotometers for tooth color measurement, investigators characterized tooth color using color tiles of the Munsell Color Order System to visually match tooth color (Clark 1931; Kato 1976). Clark made visual color assessments of the anterior teeth of 1,000 patients who came to his office for dental treatment over a period of eight years. Color assessments were made in a dark room by two observers, each making three measurements for each tooth on three consecutive days and the average of the readings was accepted as the final. Two daylight lamps with a temperature of 5,900K provided illumination through the period of the experiment. Clark found a Hue range from 6.0 YR to 9.3 Y, a Value range from 4/ to 8/ and a Chroma range from /0 to /7, and noted that as Value decreased, Chroma decreased. Similarly, Kato made visual color assessments of the upper incisors and canines of 120 subjects, composed of patients, employees, staff and students of the Tokyo Medical and Dental University School of Dentistry. Kato reported a set up using lighting with a color temperature of 6,500K positioned 45 degrees to the viewing angle. He found a Hue range from 8.75YR to 3.75Y, a Value range from 5.5/ to 8.0/ and a Chroma range from /1.0 to /5.0. As colorimeters and spectrophotometers became more reliable and compatible with clinical use, many research groups used these color measuring devices to determine tooth color and investigated its association with ethnicity, gender, age, tooth number or location on the tooth. Sproull measured the color of 33 extracted teeth using a General Electric Recording Spectrophotometer and converted the data to Munsell notations. The Hue

35 22 ranged from 7.5YR to 2.7Y, Value ranged from 5.8/ to 8.5/, and Chroma ranged from /1.5 to /5.6. (Sproull 1973) Goodkind and Schwabacher measured the color of 2380 anterior teeth from 500 subjects aged 17 to 70 using three colorimeters (Chromascan fiber-optic colorimeter, Sterndent Corp., Stamford, Connecticut). Inter and intra-operator reliability were tested and found to be acceptable. Data from a fourth Chromascan colorimeter were not used for the analysis as measurements of mean Hue and Chroma were significantly higher than the other three colorimeters. The colorimeters measured color in terms of numbers for red (R), green (G), and blue (B). These numbers were converted to a set of standard tristimulus values X, Y, and Z, based on regression equations from previous studies. The tri-stimulus values were then used to determine the Munsell Hue, Value, and Chroma, using the Davidson interpolation program (Davidson Colleagues, Tatamy, Pa.) The Hue for all teeth ranged from 4.49YR to 2.59Y with a mean of 0.69Y (s.d. 0.70), Value ranged from 5.66/ to 8.48/ with a mean of 7.43/ (s.d. 0.39), and Chroma ranged from /1.09 to /4.96 with a mean of /2.84 (s.d. 0.45) (Goodkind and Schwabacher 1987). The investigators also analyzed results for differences in tooth color between males and females, in different age groups, in different types of teeth, in different locations on a tooth, and in different population groups, all of which will be discussed in the following sections. In this study, possible bias could have been introduced from omitting data collected from the fourth colorimeter. Modeling dough was placed on the lingual surfaces of the teeth being measured to provide a uniform and neutral background. However, the color of the modeling dough was not reported. Though a sample size of 2830 teeth would be considered adequate for the number of variables tested, measurements were made on multiple teeth originating in each subject so data points are not entirely independent from another. In both the Sproull and the Goodkind and Schwabacher studies, other error could have come from converting data obtained by the instruments to HVC.

36 23 Numerous other investigators have measured the color of human teeth in vivo (see table 2-2) (Rubino et al. 1994; Hasegawa, Ikeda, and Kawaguchi 2000; Zhao and Zhu 1998; Dozic 2004; Cho, Lim, and Lee 2007; Gozalo-Diaz et al. 2007; Xiao et al. 2007). Tooth color values from one study cannot be directly compared to another study. In a review of these studies, Joiner et al attributed the large range of mean values mainly to technical factors, such as measurement technique and type of measuring instrument, while tooth factors, such as type, vitality, and area measured on the tooth, and patient factors, such as country of origin, gender, age, and socio-economic status, accounted for a smaller range of variation (Joiner et al. 2008). Cho et al showed that tooth color values derived from a colorimeter and a spectrophotometer differed greatly using the same study population (Cho, Lim, and Lee 2007). In addition, colorimeters and spectrophotometers with small port openings are prone to edge loss effects, resulting in less accurate results due to systematic error (Bolt, Bosch, and Coops 1994). Edge loss occurs when light is scattered laterally through the translucent portions of teeth (especially at the incisal thirds of anterior teeth) out of the measuring area of the instrument. Furthermore, colorimeters are known to have good precision but poor accuracy (Seghi 1989; Douglas 1997) Tooth color differences between genders Goodkind and Schwabacher found that at the middle site of teeth, the mean color values (Hue, Value and Chroma) for males (0.562Y/7.35/2.87) were significantly different than females (0.796Y/7.49/2.81). In other words, tooth color in females on average were lighter, less saturated, and less yellow (or shifting toward blue) than men. However, it would seem that the clinical differences were small (Goodkind and Schwabacher 1987). Xiao et al reported that tooth color in females were lighter and less yellow (or more blue) than men. The mean CIE L* values for females were significantly higher by 1.7 units than for males, while the mean CIE b* values for females were significantly lower by 0.9 units than for males (p < 0.05) (Xiao et al. 2007). The results of

37 24 these studies seem to be in agreement, if one considers the hue scales and the relative locations of yellow and blue hues in the Munsell and CIELAB color systems. Though these studies do report statistically significant differences in Value, Hue and Chroma, it may be questioned if these differences are clinically significant. Tooth color change with aging In the Goodkind and Schwabacher study, as mean age increased from 17 to 70 years, tooth color became less yellow and more red (from 0.761Y to 0.271Y), darker in value (from 7.54/ to 6.97/), and higher in color saturation (from /2.70 to /3.12) (Goodkind and Schwabacher 1987). Two other studies reported tooth color becomes darker and more yellow with age. In a linear regressive analysis, Xiao et al found an increase in CIE b* and a decrease in CIE L* with increasing age from 13 to 64 years (p < 0.05) (Xiao et al. 2007). Hasegawa et al, whose subjects age range from 13 to 72 years, reported significant positive correlation of CIE b* and significant negative correlation of CIE L* with age and with age (Hasegawa, Ikeda, and Kawaguchi 2000). In conclusion, results of these studies agree that tooth color darkens with age, but differ regarding whether tooth color becomes more yellow or red with age, which could be the result from sampling populations of different ethnicities. Tooth color differences from tooth to tooth Goodkind and Schwabacher, in their study of 2830 human anterior teeth, found that 1) maxillary anterior teeth (mean Hue of 1.126Y and for the maxillary central and lateral incisors respectively) were slightly more yellow than mandibular anterior teeth (mean Hue of 0.603Y and 0.695Y for mandibular central and lateral incisors respectively), 2) maxillary central incisors (mean Value of 7.70) had a higher Value than mandibular central incisors (mean Value of 7.53), 3) canines (mean Value of 7.13) had lower Values than the adjacent incisors (mean Value of 7.49), and 4) maxillary central

38 25 incisors (mean Value of 7.70) had the highest Values found among anterior teeth (Goodkind and Schwabacher 1987). Zhao et al measured the color of 410 maxillary anterior teeth in a Chinese population. Their results agreed with Goodkind and Schwabacher s study that the central incisors had the highest Values (mean CIE L* of 51.48), followed by the lateral incisors (mean CIE L* of 47.99), and then the canines (mean CIE L* of 44.53). Zhao et al further reported that canines (mean CIE a* of 1.31) were significantly more red than the lateral incisors (mean CIE a* of 0.37) (P<0.05), and central incisors (mean CIE b* of 0.15) were significantly less yellow than the lateral incisors (CIE b* of 1.96) and canines (CIE b* of 3.19) (P<0.05). In summary, these studies agree that the Value decreases from maxillary central incisor to maxillary canine. Color differences between different locations on a single tooth O Brien et al used a spectrophotometer (Hardy, General Electric Co., West Lynn, MA, USA) to record the color of the cervical, middle, and incisal thirds of 95 extracted anterior teeth. Tristimulus coordinates were determined by use of the CIE 1931 standard observer functions and standard illuminant source C, and converted to CIELAB and Munsell notation by means of graphs (Color Research Laboratory, Agricultural Marketing Service, USDA, 1964) and the method described by ASTM Standard D (1984) and then plotted. The research group found that the Hue was more yellow in the incisal region in 55% of the teeth, more red in 40%, while 5% had no difference in Hue. The Value was higher in the incisal region of 34% of the teeth, darker in 56%, while 10% had no difference. The Chroma was more intense in the incisal region of 7% of the teeth, less intense in 88%, while 5% had no difference. The research group also found a significant difference in color (ΔE) of 8.2 units between the cervical and incisal regions, 4.4 units between cervical and middle regions, and 4.9 units between middle and incisal

39 26 regions. These results should be interpreted based on the fact that tooth color was measured on extracted teeth embedded in grey caulking compound (O'Brien et al. 1997). However, they do show that color differences between different locations on a single tooth are extremely variable and that population means may not be applicable to all individuals. Goodkind and Schwabacher found that the Hue of the cervical thirds tended to be more red (5.20YR) than the incisal thirds (1.00Y) and middle thirds (0.694Y). The Chroma of the cervical thirds of the anterior teeth tended to be higher (3.27/) than the middle third (2.83/) and the incisal third (2.86/). The Value in the incisal, middle and cervical thirds were similar (/7.24, /7.43 and /7.42 respectively)(goodkind and Schwabacher 1987). Their results were presumably overall trends, and the authors did not report if these trends applied to all individuals. Ardu et al conducted a pilot study with 62 Swiss army recruits (20-21 years of age) to characterize the optical properties of 2mm of pure enamel and 3mm of enameldentin complex. A reflectance spectrophotometer (SpectroShade, Type , MHT, Verona, Italy) was used to record the CIE L*a*b* color coordinates of the central upper incisors against a white and a black background. For 2mm thick enamel, white backgrounds resulted in measurements that were lighter, more red and more yellow than measurements made with black backgrounds (ΔL* = 14.77, Δa* = 2.37 and Δb* = 6.56). For 3mm thick enamel-dentin complex, the color differences between white and black backgrounds were smaller (ΔL* = 4.24, Δa* = 1.78, and Δb* = 4.40). The research group concluded that the incisal third (which is predominantly enamel) was more influenced by the background than the middle third (which consist of the dentin-enamel complex). (Ardu et al. 2010). Dozic et al measured the color of the maxillary central incisor of 64 Danish subjects (with an average of 32.8 years of age) using a digital camera (CAMEDIA C- 2040ZOOM, Olympus, Tokyo, Japan) and a standardized head-camera set up. The color

40 27 relationships between the cervical, middle and incisal segments were measured. The data showed statistically significant linear correlations for CIE L* and CIE b* between the three segments (all r s 0.60; P<0.001). The research group concluded that by extrapolating CIE L* and CIE b* measurements of the middle segment, they could estimate the CIE L* and CIE b* of the cervical and incisal segments (Dozic 2004). However, correlations for CIE a* were weak between the three segments (0.33 r 0.45), implying that color measurements from the middle segment cannot be extrapolated for the cervical and incisal thirds reliably. In summary, the studies by O Brien and by Goodkind did not agree if the cervical thirds were more red or more yellow than the middle thirds or incisal thirds, which may be explained by Dozic s study, which showed that though the teeth had a strong correlation of CIE L* and CIE b* among the thirds, the correlation of CIE a* was weak. O Brien study showed that color differences between different locations on a single tooth are extremely variable and that population means may not be applicable to all individuals The results of O Brien and Goodkind s studies should be interpreted with caution as these studies used modeling compound on the lingual surfaces as a background, which would have influenced the color of the teeth, especially in the incisal third, as demonstrated by Ardu. Population differences Goodkind and Schwabacher found no statistically significant differences between populations for color for the incisal third and in Value and Chroma for the middle and cervical sites. 156 teeth from subjects born outside the United States had slightly yellower middle (0.13 Hue units) and cervical thirds (1.5 Hue units) than native Minnesotans (Goodkind and Schwabacher 1987).

41 28 Visual shade matching Basic concepts Light sources Illumination is one of the most important factors that affect shade matching. Light sources with lower or higher color temperatures produce different color distributions, resulting in different spectral reflectance profiles in the shade guide and teeth being matched. For visual shade matching, it is recommended that the color temperature for the light source should be around 5,500 K and the color rendering index (CRI) should be greater than 90 (Bergen and McCasland 1977; Corcodel et al. 2009; Preston 1980). Light sources with lower or higher color temperatures produce different color distributions in the shade guide and the teeth being matched, which reduce an observer s ability to match shades accurately (Park, Lee, and Lim 2006; Gokce et al. 2010) Park et al measured the color profile of two types of shade guides (VITA Classical, VITA Zahnfabrik, Bad Sackingen, Germany, and Chromascop, Ivoclar, Vivadent, Schaan, Liechtenstein) relative to the standard illuminants D 65 (6500 K), A (2856 K), and F2 (4230 K) using a reflective spectrophotometer (Color-Eye 7000A; GretagMacbeth Instruments Corp). Mean color differences under D 65 and F2 illumination, for VITA Classical and Chromascop shade tabs were 1.65 ΔE units and 2.45 ΔE units respectively. Under D 65 and A illumination, VITA classical and Chromascop shade tabs showed a mean color difference of 2.22 ΔE units and 2.71 ΔE units respectively. All mean color differences recorded were significant (p < 0.05) (Park, Lee, and Lim 2006). Lee et al confirmed Park s findings. The research group measured the shift in color of a VITA 3D Master (VITA Zahnfabrik, Bad Sackingen, Germany) shade guide under D 65, A and F9 (4150 K) illumination using a spectroradiometer (PR-670 SpectraScan; Photo Research Inc, Chatsworth, California). The color differences under

42 29 D65 and A illumination ranged from 4.0 to 9.1 ΔE units, while the range of color difference under D65 and F9 was 3.2 to 8.5 ΔE units (P<0.001). CIE L* decreased and both CIE a* and CIE b* increased when the illuminant changed from D 65 to A or F9 (Lee et al. 2011). Barna et al carried a study to investigate the effect of light intensity on the ability of observers to detect color difference within the color range of teeth. Lighting (Verd-A- Ray criticolor color corrected tubes, Verda-A-Ray Corporation, Toledo, Ohio) was set up in a neutral grey colored test room with a capacity of 156 cubic feet, producing a light intensity of 75, 150, 225, and 300 foot-candles at the viewing site, as measured by a light meter (Type 214 light meter, General Electric, Fairfield, Connecticut). A set of 25 color chips simulating the color range of teeth based on the Munsell color system was assembled. 50 dentists were then asked to match five color chips that were randomly chosen from a second set of identical color samples with the first set of color samples. The order of light intensities and arrangement of the color samples were randomized. The research group found no difference in visual shade matching ability within the range of 75 to 300 foot-candles (Barna et al. 1981). No studies have investigated the effect of light intensities below 75 foot-candles on visual shade matching performance. Shade matching environment Light that falls on teeth come not only from the light source, but also from nearby surfaces such as ceilings, walls and objects in the form of reflected light. In order to minimize the changes in quality and quantity of reflected light, these surfaces should be of high value and neutral in color. (Preston 1980). Shade guides Tooth shade guides provide a common platform for communicating tooth color. Thus far, the dental governing bodies have not agreed on a standardized tooth shade

43 30 guide. Two of the most commonly used shade guides are the VITA Classical shade guide and the VITA 3D-Master shade guide. VITA Classical guide The VITA Classical (VITA Zahnfabrik, Bad Sackingen, Germany) shade guide is one of the most commonly used guides. It has a total of 16 shades that are divided into four groups based on hue. The manufacturer labels group A reddish brown, group B reddish yellow, group C gray, and group D reddish gray. Shades within each group are based on changes in Chroma and Value, in which increasing Chroma and decreasing Value correspond to higher designated numbers. Preston and Bergen reported color measurements for the VITA Classical shade guide. They used a General Electric Recording Spectrophotometer (Fairfield, Connecticut) and described the color in Munsell notation. They did not report color dimensions for shade A3.5 or D4, possibly because they measured an early version of the guide. Further methodology is not given. For all groups, Hue fell within 1.0Y and 4.6Y. Group A Hue ranged from 1.0Y to 2.3Y (the group closest to yellow-red overall), group B ranged from 2.4Y to 4.6Y (the most yellow group overall), group C ranged from 1.5 Y to 3.2Y, and group D ranged from 1.5Y to 2.6 Y. Group A did not exhibit any consistent trends in Hue change across the group. The rest of the groups showed a trend where a larger designated number corresponded to the Hue shade that was closer to 1Y (more toward yellow-red). Value ranged from 6.4/ to 8.1/. Within each group, a larger designated number corresponded to a lower Value. Chroma ranged from /1.7 to /3.7. Within each group, a larger designated number corresponded to a higher Chroma number. Within each group, Hue, Value and Chroma were not uniformly spaced within the Munsell color space (Preston and Bergen 1980). O Brien et al also measured the color of the VITA Classical shade guide using a spectrophotometer (Beckman Model ACTA CIII, Beckman Instruments, Inc., Fullerton,

44 31 CA)(O'Brien, Groh, and Boenke 1990). The color temperature of the light source was not reported. Measurements were made in the middle third of each shade tab. The color coordinates were converted to Munsell notation by means of graphs (Color Research Laboratory, Agricultural Marketing Service, USDA, 1964), which was described by ASTM standard D (1984). To eliminate the translucency of the shade guide teeth, the lingual surfaces of the shade guide teeth were coated with white barium sulfate to control the background. They reported similar results for shade guide color as Preston and Bergen. For all shades, Hue ranged from 1.3Y to 5.1Y. Group A Hue ranged from 1.6Y to 4.5Y, group B ranged from 2.3Y to 5.1Y, Group C ranged from 1.6Y to 4.3Y, and group D ranged from 1.8Y to 3.7Y. Value ranged from 6.3/ to 7.8/ and Chroma ranged from /1.6 to /3.2. As the designated numbers increased across each group, Hue and Value showed a decreasing trend (toward yellow-red) while Chroma showed an increasing trend. Paravina et al performed colorimetric analysis of a VITA Classical and a VITA 3D Master shade guide using a colorimeter (Minolta CR-321 photoelectric colorimeter, Tokyo, Japan) and showed through graphs of color distribution and ranges that VITA Classical was more unevenly spaced in the CIELAB color space than the VITA 3D Master (Paravina, Powers, and Fay 2002), However, no statistical analysis was used to validate this claim. Problems with VITA Classical shade guide Limited range compared to human teeth In principle, shade guides should adequately represent the entire Hue, Value, and Chroma range of natural teeth. However, this is not the case for the VITA Classical shade guide when comparing published tooth color data (Clark 1931; Kato 1976; Sproull 1973; Goodkind and Schwabacher 1987) with published shade guide color data (Preston and

45 32 Bergen 1980; O'Brien, Groh, and Boenke 1990). The VITA Classical shade guide shows deficiencies in RY Hues, Hues extends too far Y, range of Value is too narrow, and Chroma is deficient. How well a guide represents all tooth shades can also be expressed in terms of coverage error. Coverage error is the average color difference between the closest shade guide match and the tooth s actual/perceived color (O'Brien, Boenke, and Groh 1991). O Brien et al used the color data of a VITA Classical shade guide from his previous study in 1990 and compared it with the published data of 335 extracted human teeth from three previous studies (Marui 1968, Sproull 1973, Lemire and Burk 1975). The study by Marui had a sample size of 72 teeth and visual matching was performed using the Munsell color standards. The study by Sproull (33 teeth) and the study by Lemire and Burk (230 teeth) used spectrophotometric measurements. The color measurements (in Munsell notation) of all 335 extracted teeth were plotted graphically to demonstrate the distribution of tooth color. The Munsell notations were also converted to CIE L*a*b* parameters. O Brien et al did acknowledge the limitations of pooling data derived from different studies that used different methods of recording tooth color. The research group calculated the coverage error of the VITA Classical shade guide by comparing each tooth color with each shade guide color and determining the color tab with the least color difference (ΔE units), and found a coverage error of 3.02 ΔE units (s.d. 2.26) (O'Brien, Boenke, and Groh 1991). The percentage of teeth that had a ΔE greater than 3.5 for the closest match was 25% for the VITA Classical shade guide. The ΔE of 3.5 as a threshold was based on Ruyter et al that color differences less than 3.3 were acceptable, and on Johnston and Kao that the average color difference judged to be a match was 3.7. Bayindir et al measured the color of 359 teeth in 120 human subjects and the color of three different shade guide systems: VITA Classical shade guide, Chromascop, and VITA 3D Master. The research group used a spectroradiometer (PR 705; Photo

46 33 Research Inc., Chatsworth, California) with a set up in a 0-degree observer and 45-degree illumination configuration to make all color measurements. By ensuring no aperture was between the light source, measuring instrument and object, edge loss was avoided. The VITA Classical shade guide showed a coverage error of 5.39 ΔE units (S.D. 3.14; range ) (Bayindir et al. 2007). The author did not report the percentage of teeth that had ΔE greater than a published clinically acceptable threshold. In these two studies there was a large difference in the coverage error reported for the VITA Classical shade guide, which could have been due to the several differences in their methodology. O Brien et al used the color of extracted teeth from previous studies while Bayindir et al measured the color of teeth in vivo. In addition, O Brien et al used a color measurement instrument that suffered from edge loss and coated the lingual surfaces of the shade tabs with barium sulfate. Bayindir et al study set up did not suffer from edge loss and the lingual surfaces of the shade tabs were not coated. Although there was a large difference reported for coverage error in these studies, color data and graphs from the two studies indicated similar deficiencies in Hue, Value and Chroma, as previously described. Organization of the shade guide The VITA Classical shade guide is organized into groups according to hue, with gradations of Chroma and value within each group. However, Preston and Bergen suggested that Value should be evaluated first, followed by Hue, and then Chroma (Preston and Bergen 1980). The company that produced this shade guide subsequently provided a perceived light to dark ordered arrangement only, but the arrangement does not organize Hue and Chroma in any systematic way. Yilmaz et al investigated the effect of the hue-ordered and value-ordered arrangements of the VITA Classical shade guide on the repeatability and accuracy of visual shade matching of 35 restorative dentists with at least two years of clinical

47 34 experience. A viewing booth with D 65 lighting and a gray testing background were set up for this experiment. Thirteen male and twenty-two female subjects where asked to use both arrangements to match seven arbitrarily selected test shade tabs taken from another VITA Classical shade guide. Each subject performed the shade matching test of each arrangement twice, with at least one hour interval between each test. Though the subjects did not know the order of the test shade tabs, there was no mention of randomizing the order of the test shade tabs after each shade matching test so as to prevent the subjects from remembering the order. When the hue-ordered shade arrangement was used, 58% of the shade selections were correct. When the value-ordered shade arrangement was used, 57.6% of the shade selections were correct. In terms of repeatability, 55.5% of the selections were repeated with the hue-ordered arrangement compared to 54.3% with the value-ordered arrangements. The research group found no difference (p > 0.05) in the accuracy and repeatability of shade matching with Hue/Chroma-ordered and valueordered arrangements (Yılmaz et al. 2011). Manufacturer variation and construction methods / materials The color of shade tabs of a single shade designation from different shade guides can differ due to manufacturing variation. King and derijk investigated the variation in color of all 16 shades in 25 VITA Classical shade guides using a XE scanning spectrocolorimeter (Hunters Associates Laboratory, Reston, VA), which measures the reflectance at 45 degrees and illuminates the sample at 0 degrees. A shade tab holder, which was used to ensure repeatability of sample positioning, was centered over the port opening which was 3.2mm in diameter. The set up was tested for reproducibility by testing all 16 shades of a shade guide ten times. For each shade, the reproducibility test showed a range of 0.08 to 0.69 ΔE units and the standard deviation varied from 0.02 to 0.22 ΔE units. For the 25 shade guides, the range of color variation within each shade

48 35 varied from 0.75 to 3.05 ΔE units, with the standard deviation varying from 0.22 to 0.54 ΔE units. The range and standard deviation of color variation for the 25 shade guides was significantly larger (p < 0.05) than the reproducibility test. The research group also noted that the difference in color between shade C1 and C2 (2.19 ΔE units) was smaller than the range of color variation between shade tabs of shade C1 (3.05 ΔE units) (King and derijk 2007). Cal et al used the Adobe Photoshop 4.0 graphic program to analyze the brightness and RGB coordinates of images of three shade guides (Chromascop, Ivoclar-Vivadent Schaan, Liechtenstein) created by the same manufacturer and reported that under identical lighting conditions the color of a single shade could vary widely among the guides (Cal et al. 2004) However, this study only had a sample size of three shade guides, did not report any ΔE values, and was not supported by any statistical analysis. VITA 3D Master guide The VITA 3D-Master shade guide system was specifically designed and organized to allow independent comparisons of value, chroma and hue. The shade guide has 26 shades and is divided into five groups based on value, with 1 designating the lightest shade group and 5 designating the darkest shade group. Each value group has 2-3 levels of chroma depending on the hue and 3 choices of hue (a single base hue, more yellow, and more red-yellow). Two research groups measured the color distribution of the VITA 3D Master shade guide. Though both research groups used different experimental set ups and measuring instruments, both found that the value, chroma and hue were ordered according to manufacturer s claim, but the intervals between adjacent shades were not uniform, based on the CIELAB color measurements. Ahn et al measured the color distribution of the VITA 3D Master shade guide using a reflection spectrophotometer (ColorEye 7000A, X-Rite, Inc and GretagMacbeth AG/LLC). The mid labial portion of all 26 shade tabs were polished to produce a flat

49 36 measuring surface to remove any possibility of variation due to contour of the labial surface. A light tight box was used to exclude all external light and a D 65 lighting was used in a diffuse illumination and 8 degree observation configuration. Measurements were repeated 5 times for each shade and averaged. The research group found that the relative distributions of hue and chroma of the tabs within the same value group were ordered but values between different value groups overlapped in several instances. However, they also found that the differences in value and chroma between adjacent tabs were not uniform (Ahn and Lee 2008). Lee et al measured the color distribution of the VITA 3D Master shade guide using a spectroradiometer (PR-670 SpectraScan, Photo Research Inc, Chatsworth, California). The research group found that value, hue and chroma trends for the shade guide system were consistent with the manufacturer s claim. However, the intervals in value and chroma between adjacent tabs were not uniform. They reported CIE L* values for each shade that were 15 to 20 units higher than those reported in Ahn et al study. (Lee, Yu, and Lim 2010). Though the results reported a standard deviation for each shade measured, there was no mention of how many times the measurements were repeated, or if one or more shade guides were used. The research group states that the experiment used diffuse illumination and zero degree observation geometry within a light tight box. However, they describe a set-up in which two D 65 lamps were fixed just adjacent to the spectroradiometer at a distance of 355mm from the measured sample. This could effectively result in direct illumination close to 0 degrees instead of a diffused illumination. Bayindir et al investigated the coverage error of VITA Classical and VITA 3D Master in a sample population of 120 subjects, ages 18 to 85 years old, in the State of Ohio. Five sets of shade guides from each system and the central incisors, lateral incisors and canines of the subjects were measured with a spectroradiometer (PR 705, Photo Research Inc, Chatsworth, California) using 45 degree illumination and 0 degree

50 37 observation. The research group found that the coverage error of VITA 3D Master (3.93 ΔE units) was significantly lower than that of VITA Classical (5.39 ΔE units) (Bayindir et al. 2007). Limitations in visual shade matching Standardized shade matching procedures based upon physical and physiologic principles have been described to facilitate accurate and consistent shade matching by dentists (Preston and Bergen 1980). Despite this, problems associated with visual shade matching have been documented in the literature. Dehydration of teeth Russell et al investigated the changes in color of central incisor teeth when a rubber dam was placed for 15 minutes and when impressions were made with polyvinylsiloxane impression material. A reflectance spectrophotometer (Monolight model 6800) with a helium-neon laser light source at nm and a fiber-optic bundle of 4mm in diameter was used to measure tooth color. Calibration was performed with a standard white tile (British Ceramic Association, color standard) before each measurement session. A group of seven dental students had the color of their upper central incisors measured before placement of a rubber dam and measured again 15 minutes after letting the teeth dry out. The rubber dam was removed and three more color measurements were made at intervals of ten minutes. A second group of seven dental students repeated the same color measurements as the first group, except that a polyvinylsiloxane impression, which took six minutes to set, was made instead of placing a rubber dam. The research group found that the CIE L* value of the teeth of subjects in the rubber dam group and impression group significantly increased from to and to respectively. In both groups, the teeth returned to their baseline color 30 minutes after the teeth were allowed to rehydrate. (Russell, Gulfraz, and Moss 2000).

51 38 Training and experience of the person taking a shade match Several studies have investigated the effect of clinical experience and training on visual shade matching performance. Hammad investigated intra-observer repeatability of ten prosthodontists and ten general practitioners who used VITA Classical and VITA 3D Master shade guides. Each clinician was required to shade match the maxillary right canines of 20 subjects using both shade guide systems under controlled lighting and shade matching protocols. Shade selections were repeated a month later by the same clinicians on the same subjects. The orders of teeth matched by each clinician for both shade matching sessions were randomized. The results showed that when the VITA Classical shade guide was used, the intra-observer repeatability of prosthodontists (62%) was significantly higher (p < ) than that of the general practitioners (35%). However, when VITA 3D Master shade guide was used, the intra-observer repeatability of prosthodontics (67%) and general practitioners (60%) were not significantly different. The investigator reported that none of the clinicians had any experience with the VITA 3D Master prior to this study and that training was provided by the investigator a month before the start of the study. While there was no significant change in the intra-observer repeatability of prosthodontists when using either shade guides, the intra-observer repeatability of general practitioners improved significantly when using the VITA 3D Master (p < ) (Hammad 2003). Della Bona et al reported on the effect of professional experience on shade matching performance. 600 shade matching participants were split into three groups: nondental observers, dental students and dentists. Participants were asked to shade match using the VITA Classical and the VITA 3D Master under two lighting conditions, cool white fluorescent lighting and natural sunlight. Non-dental observers matched against eight test shade tabs from each of the shade guide systems that were of equivalent color. Dental observers (dental students and dentists) matched in vivo, a right upper central incisor. An intra-oral spectrophotometer (VITA EasyShade) was subsequently used to

52 39 measure the shade of the incisor. The research group found that the dentists had a significantly higher percentage of matches compared to the other participants regardless of type of shade guide used or the lighting conditions (Della Bona et al. 2009). Capa et al had 120 participants (prosthodontists, restorative dentists and technicians, other dental specialists, general practitioners, dental assistants, students and lay people) match three selected shade tabs from a VITA 3D Master shade guide (2L1.5, 1M2 and 2R1.5) with another VITA 3D Master shade guide. Shade matching was conducted between 10:00am and 2:00pm, with cloudy and rainy days excluded from the study. Participants shade matched at a table with a neutral gray background situated next to a window that received daylight. No training on the use of the VITA 3D Master shade guide was given to any of the participants. The results showed that 53.3 percent of the prosthodontists, restorative dentists and technicians had an exact match with 1M2, compared with 30 percent of the other dental specialists and general dentists, and 20 percent of non-dentists (p = 0.017). No significant differences were found between the three groups of participants for shades 2L1.5 and 2R1.5. The research group also analyzed the data to see if the number of years of professional experience was associated with better shade matching ability, and they found that when shade matching 2L1.5, participants who matched correctly had an average of 10 years of professional experience, while those who did not match correctly had an average of 5 years of professional experience (p = 0.003). (Capa et al. 2010). This study did not produce any strong conclusion because significant differences in shade matching success occurred in only one of the three test shades. The color range of test shades tested was limited and thus not generalizable. Sim et al investigated the differences in color perception among four groups of dental personnel: 10 dental technicians, 15 final year dental students 15 general practitioners and 10 prosthodontists. Observers were asked to match seven test tabs of shades (Z100 shade guide, 3M Dental Products, St Paul, MN 55144, USA) against a

53 40 VITA Classical shade guide under color-corrected lighting (Trucolor TLM 40W/33RS, Phillips, Le Mans, France). The color of the test and VITA shade tabs were measured using a small area colorimeter (Dental Colorimeter, Minolta Camera, Tokyo, Japan). The color difference in ΔL*, Δa*, Δb* and ΔE between the color of the test shade tabs and the VITA shade tabs were calculated. Statistical analysis showed that there was no significant difference in ΔL*, Δa*, Δb* and ΔE values among the different groups of dental personnel except for shade C4. For this shade, there was a significant difference in ΔE* values between dental technicians (6.95±2.23) and the other groups (Dental students 5.53±0.71, General Practitioners 5.53±0.71, and Prosthodontists 5.35±0.00) (p<0.01). This difference was contributed mainly by a disparity in ΔL* values, in which dental technicians (5.50±2.64) chose higher value shades than dental students (3.89± 1.11), general practitioners (3.89± 1.11), and prosthodontists (3.60± 0.00). The authors concluded that there was no difference in shade matching performance related to level of experience (Sim, Yap, Teo 2001). Haddad et al investigated the effect of level of experience on shade matching ability. Their study involved 15 universities in the following 9 countries: Austria, Czech Republic, France, Germany, Hungary, Lebanon, Slovenia, Spain and United States of America. 614 subjects (319 dental students and 295 dental professionals) with normal color vision used a VITA 3D Master shade guide to shade match another VITA 3D Master shade tab under color corrected light (Dialite Color, Eickhorst, Germany) with a color temperature of 5500K. Subjects shade matched 15 test shade tabs and the sum of the color differences between the test shade tabs and the selected shade tabs were calculated. The CIELAB values of the VITA 3D Master shade guide were provided by the manufacturer. Subjects who had low scores meant that they had better shade matching results than those who scored high. The difference in scores between dental students 42(S.D. 20) and dental professionals 39(S.D. 21) was not statistically significant. (Haddad, et al. 2009).

54 41 Metamerism Metamerism occurs between teeth and shade guides under different illuminations. Corcodel et al investigated the metameric effect of illuminant A (incandescent light) and TL84 illuminant (store/office light) on 49 natural teeth of 37 subjects. The closest matching shade (ΔE < 2 units) of the VITA 3D Master shade guide were determined using an intra-oral spectrophotometer (VITA EasyShade Compact, VITA Zahnfabrik, Bad Sackingen, Germany). The shade matches were verified visually by two experienced prosthodontists to have a good color match with the corresponding VITA 3D Master shade under controlled daylight lighting conditions. The spectral reflectance curves for teeth and shade tabs were recorded from 400 to 700nm, in 10nm intervals, using software provided by VITA Zahnfabrik, which is not available commercially (SHADERITE2HW, JJL Technologies LLC, N. Austin, TX, USA). CIELAB values for the 49 teeth and their corresponding shades were calculated from the measured spectral reflectance curves for illuminant A, TL, and for D 65 as the reference illuminant. To evaluate metamerism, differences in L*, a* and b*, as well as the ratio of hue angles, h*, were compared with D65 as the reference standard for both A and TL84. Hue angle ratio was calculated using the following formula: hue-angle ratio = h*(a or TL illuminant) h*(d65) for tooth h*(a or TL illuminant) h*(d65) for tab A hue angle ratio value of 1 indicates no metameric effect. The research group found no statistically significant difference in mean CIE L* under the three illuminants for teeth or shade tabs. Mean CIE a* values for teeth and shade tabs were significantly more red for illuminant A compared to TL84 illuminant (p < 0.001). Mean CIE b* values for teeth were significantly more towards blue for TL84 illuminant only. Comparing illuminant A with illuminant D65, the mean change in hueangle for teeth was 1.98 degrees more towards red than for the tabs. The mean ratio of hue-angle changes for illuminant A was 1.17 (range: ), indicating that the hue

55 42 change was 1.17 times greater for teeth than for shade tabs. A positive ratio indicates that the hue changes were in the same direction (towards red) for teeth and tabs. Comparing illuminant TL84 with illuminant D65, the mean change in hue-angle for teeth was 1.72 degrees more towards red than for tabs, while the mean ratio of hue-angle changes for illuminant TL84 was 1.54 (range: ). However, no statistical analysis was reported to indicate if the greater shift in hue-angle for teeth than tabs was of statistical significance. Color vision deficiencies About 8% of males and about 2% of females have genetically determined defective color vision. These people have diminished ability to discriminate red-green or blue-yellow aspects of colors because their retinas do not have one or more cone types (Rosenthal and Phillips 1997). Davison and Myslinski investigated the effect of defective color vision on shade matching ability in terms of Hue, Value and Chroma selection. Subjects from the University of Maryland Dental School were screened for defective color vision using the Pseudoisochromatic slide test (Dvorine, second edition, 15 plates, American College of Prosthodontists). Twenty dental student and dentists with defective color vision, and 20 randomly selected subjects from the sample population with normal vision were chosen for the study. The study used three Munsell color tab grids, with each grid having a constant for a single parameter: Hue 2.5Y or Value 7/ or Chroma /4. The other parameters were variable: Hue ranged from 7.5 yellow/red to 7.5 yellow, Value ranged from 4/ to 8/, and Chroma ranged from /1 to /12. Each variable parameter had 5 increments with uniform intervals. Five shades from each grid were selected as test shades. All subjects attempted to find visual matches for the five test shades within each of the Munsell grids under a full spectrum light source (Durolite Bulb, Durotest, North Bergen, N.J.), first with the Hue constant grid, then with the Value constant grid and

56 43 finally with the Chroma constant grid. The study found that the defective color vision group had a Hue matching error mean of 2.50 incorrect matches (1.70 s.d.), which was significantly higher than the control group s mean of 0.10 (0.30 s.d.) (p < 0.01). The Chroma matching error mean for the defective color vision group was 0.85 (0.81s.d.), which was significantly higher than the mean of the control group of 0.20 (0.40 s.d.) (p < 0.01). No difference in the Value matching error was noted between the defective color group and the control group (Davison and Myslinski 1990). This study was well controlled and demonstrated that subjects with defective color vision could have greater error in color matching Hue and Chroma than normal subjects. Gokce et al, studied effect of different illuminants on defective color vision and normal color vision subjects. 24 male subjects (12 control subjects and 12 subjects with defective color vision), between years of age with at least 2 years of clinical experience, performed shade matching in a light booth under D 65 lighting (6504K) and tungsten filament lighting (2850K). Defective color vision was screened using the standard pseudo-isochromatic test plates (Ishihara, 24-plate, 1993 edition). Subjects were asked to match two identical sets of 16 porcelain discs (Omega 900 classical kit, VITA Zahnfabrik) that represented the 16 shades of the VITA Classical shade guide. Pairs of the same shade were measured with a colorimeter (XL-20 Colorimeter, BYK-Gardner USA, Columbia, Md) to ensure uniformity between the sets. Each subject performed the shade match with the two different lightings on separate days. They found that the group with defective color vision performed poorly under D65 lighting conditions with a mean of 4.92 correct shade matches out of 16 matches (1.73 s.d.) but improved significantly under tungsten filament light with a mean of 9.00 correct shade matches (1.76 s.d.). On the other hand, the group with normal color vision performed poorly under tungsten filament light with a mean of 3.33 correct shade matches (2.77 s.d.) but performed well under D65 lighting with a mean of correct shade matches (1.88 s.d.) (Gokce et al. 2010). One limitation in this study was that of the twelve subjects with defective color

57 44 vision, eleven had deuteranopia (green weakness or an absent M-cone) and only one had protanopia (red weakness or absent L-cones), suggesting that this study might be more relevant for deuteranopia. These findings seem to suggest that subjects with defective color vision, deuteranopia in particular, may improve their color matching performance by using lower color temperature lighting. In contrast, Ethell et al, studied the effect of defective color vision on shade matching accuracy using 10 subjects with defective color vision and 20 subjects with normal vision (Ethell, Jarad, and Youngson 2006). This study used only male subjects as defective color vision is an x-linked recessive trait, hence it was easier to get male subjects. Even then, the study only managed to get 10 subjects with defective color vision. To increase the power of the study, two non-defective color vision subjects were tested for each defective color subject. For the shade matching test, 10 shade tabs were randomly taken from one VITA Classical shade guide and its label covered. The subjects were provided with another complete VITA Classical shade guide to match the test tabs under standard D65 lamp lighting conditions. No significant difference in shade matching performance was found between the groups (P=0.588). The study did not report using any color measuring device to ensure that the same shade designations from the two shade guides had no color difference. In conclusion, the work done by Gokce et al and Davison and Myslinski suggest that defective color vision could affect negatively shade matching performance under standard daylight conditions. However, the Ethell et al study found no negative impact of defective color vision on shade matching performance. Gender differences Haddad et al investigated the effect of gender on shade matching ability. Their study involved 15 universities in the following 9 countries: Austria, Czech Republic, France, Germany, Hungary, Lebanon, Slovenia, Spain and United States of America. 614

58 45 subjects (305 females and 309 males) with normal color vision used a VITA 3D Master shade guide to shade match another VITA 3D Master shade tab under color corrected light (Dialite Color, Eickhorst, Germany) with a color temperature of 5500K. Subjects shade matched 15 test shade tabs and the sum of the color difference between the test shade tab and the selected shade tab were calculated. The CIELAB values of the VITA 3D Master shade guide were provided by the manufacturer. Subjects who had low scores meant that they had better shade matching results than those who scored high. The research group found that females had a mean score of 38, median of 36 and standard deviation of 20, which was significantly lower (p < ) than males, who had a mean score of 44, median of 42 and standard deviation of 21. Curd at el also investigated the effect of gender on shade matching ability. 216 dental students (165 males and 51 females) were recruited as subjects to shade match 14 VITA Classical shade tabs with another VITA Classical shade guide of the same manufacturing batch, against a gray background under D65 lighting conditions (Demetron Shade Light, Kerr Corp, Orange, California). For each test shade tab, a subject was given five shade tabs to choose from, of which one was the same shade, another was an outlier, and three others were nearby shades. The research group found no difference in shade matching performance with respect to gender. The male subjects scored a mean of (1.9 s.d.) correct matches while the female subjects scored a mean of (1.9 s.d.) correct matches (Curd et al. 2006). Comparing the two studies, the study by Haddad et al used the sum of the color differences between test shade tab and selected shade tab from 15 test shade tab matches, which demonstrated more sensitivity to differences compared to Curd in which only the same shade match was considered correct. Haddad et al may not have had a significant difference between male and female if the sum of the color differences were averaged over the 15 test shade tabs or if the independent t-test was used instead of the Mann-U Whitney test, because of the large standard deviations reported in their study. Subjects in

59 46 the study by Haddad et al had to choose a shade from a choice of 26 shade tabs, which was more clinically relevant to shade matching than the study by Curd et al, in which subjects had to choose a shade from a choice of only five shade tabs. Shade matching instruments Colorimeters Colorimeters illuminate a specimen and measure red, green and blue light reflected back to the instrument. The relative amounts of red, green, and blue are termed the tristimulus values, and they define the color of the object. Tristimulus values determined by colorimetry do not generally agree with those calculated from spectrophotometric data or those based on the 1931 CIE standard observer (Wyszecki 1982) and may be affected by the age of the red, green, or blue filters (Paravina 2004). Colorimeters are commonly used as color difference meters in color production control, which requires good precision and high accuracy in color difference measurement. Seghi et al evaluated the performance of three color measuring instruments, one of which of them was a colorimeter (CR100 Chroma Meter, Minolta Corp., Ramsey, NJ), while the other two were spectrophotometers (Match-Scan, Diano, Rochester, NY; Spectrogard, Gardner/Neotec, Silver Spring, MD). Twelve shades of opaque and body (translucent) porcelain disks (Vita VMK 68, Vita Zahnfabrik, Sackingen, West Germany) were fabricated and used as test samples. To assess the accuracy of the colorimetric instruments, the research group compared the color data with a reference instrument (Hardy-type Recording Spectrophotometer, General Electric, CT). The precision of the instruments was evaluated by determining the short-term repeatability (less than 3 hours). The research group found all three instruments to have good repeatability, with the mean values ranging from 0.09 to 0.13 ΔE units and the standard deviations ranging from 0.04 to 0.12 ΔE units. The colorimeter performed significantly better (P<0.01) in mean color difference measurements than the two spectrophotometers and had a mean of 0.29 (0.25

60 47 s.d.) ΔE units from the reference instrument for the opaque porcelain samples and a mean of 0.25 (0.21 s.d.) ΔE units for the translucent porcelain samples(seghi, Johnston, and O'Brien 1989). These results demonstrate the precision and accuracy in color difference of the colorimeter, which can be used in dentistry to determine the color difference between a dental restoration and the patient s tooth, or used to determine the closest matching shade with the patient s teeth. Okubo et al compared the ability of a computerized colorimeter (Colortron II, Light Source Computer Images, Inc., San Francisco, California) and conventional visual shade matching to correctly pair shade tabs from two shade guides of the same system. Visual shade tab pairing was performed by 31 participants (7 dentists, 7 dental laboratory technicians, 17 dental students) using two sets of VITA Classical shade guides under controlled lighting conditions against a black background. Half of the participants were tested again two months later to determine repeatability. Each shade tab was measured three times using the test colorimeter and the average CIE coordinate measurement was recorded. The shade tab from one shade guide that had the smallest color difference (ΔE) to a shade tab from the second shade guide was considered a shade match using the colorimeter. 25 sets of readings were taken for each shade tab to determine repeatability of the colorimeter. Okubo et al found that the colorimeter matched 50% of the shade tabs to their corresponding tabs on the second guide, while the 31 participants matched 48% of the shade tabs. The colorimeter demonstrated perfect repeatability (correlation coefficient r=1.00) while participants demonstrated fair repeatability (correlation coefficient r=0.60) (Okubo et al. 1998). The difficulty in matching the correct shades may have been due to color differences between the tabs on the different guides. Shade tabs of different shade designation on one guide can be closer in color than tabs of the same shade designation on another guide (King and derijk 2007). Nevertheless, this study by Okubo et al demonstrated that the colorimeter had better repeatability than visual shade matching.

61 48 Kuzmanovic and Lyons compared the shade matching performance of a colorimeter with that of a conventional visual shade matching. Three dentists independently determine the shade of the maxillary right central incisors of ten subjects using a VITA Classical shade guide under natural daylight. Each of the three dentists measured the color of the ten incisors with a colorimeter (ShadeVision, X-Rite, Grand Rapids, Michigan) To determine the strength of agreement using correlation, each shade tab in the VITA Classical shade guide was assigned a ΔE value in relation to B1 shade tab, according to data provided by Paravina (Paravina, Powers, and Fay 2001). Although unstated by the authors, this may have been done to transform VITA Classical shades, which are categorical data, into continuous data necessary for correlation tests. They found poor agreement between the two shade matching methods (correlation value = 0.27). All three dentists selected the same shade for 40% of the teeth, two dentists selected the same shade for 30% of the teeth, and all three dentists disagreed on the shade for 30% of the teeth (Kuzmanovic and Lyons 2009). The presumed transformation of VITA Classical shades (categorical data) into ΔE values (continuous data) for use in intra-class correlation weakens the strength of the analysis and validity of the results. Wee et al did an in vitro study to assess the ability to duplicate the shade of ten extracted maxillary anterior teeth with dental porcelain using three distinct protocols. Each protocol utilized a different method for shade matching, along with each manufacturer s corresponding porcelain system. Three methods of shade matching were used to determine the shade of ten extracted maxillary anterior teeth: a colorimeter (ShadeEye-EX Chroma Meter, Shofu, Kyoto, Japan) and conventional visual shade matching method performed by three dentists (a general dentist, a prosthodontist, and a maxillofacial prosthodontist) using VITA Classical and also VITA 3D Master shade guides. For the VITA Classical shade guide, two out of the three dentists had to select the same shade for that to be the selected shade. A consensus on the selected shade had to be done if all three dentists selected different shades. For the VITA 3D Master, the median

62 49 of the three shades chosen by the three dentists was used as the final selected shade, because the three parameters of shade selection (value, chroma, and hue) are selected independently of each other using this guide. A third year dental student fabricated a total of 30 porcelain shade tabs which where analogous to crown restorations (3 groups of 10 samples) based on the shade results of shade selection from each of the three assessment methods. Three corresponding color matching porcelain systems were used for the fabrication of the porcelain specimens,: 1) Vintage Halo porcelain system for the ShadeEye-Ex Chroma Meter, 2) VITA VMK 68 porcelain system for the VITA Classical shade guide, and 3) VITA Omega 900 porcelain system for the VITA 3D Master. The three dentists independently assessed the clinical acceptability of the shade match of these porcelain specimens against the corresponding extracted teeth. The shade match could be a match, a mismatch but acceptable, or a mismatch that was unacceptable. The investigators found that the porcelain specimens that used the color selected by the colorimeter resulted in 40% clinical acceptable matches, compared with 46.67% for porcelain specimens whose color was visually selected using VITA Classical shade guide, and 56.67% for porcelain shade tabs whose color was visually selected using VITA 3D Master shade guide (p = ) (Wee et al. 2000). One limitation is that this study introduced multiple variables (method of shade matching, porcelain type) that may have influenced the final shade of the fabricated porcelain tabs such that one is not able to identify the contribution of each. However, it does represent a clinically realistic scenario. Another limitation is that a third year dental student with no prior experience in porcelain work made the specimens compared to utilizing an experienced laboratory technician. Li and Wang compared visual shade matching and instrumental shade matching using an intra-oral colorimeter (ShadeEye NCC Chroma Meter, Shofu, Kyoto, Japan). They fabricated metal ceramic crowns for 20 subjects based on the results of each shade matching method and then measured the color difference between the crowns and the

63 50 subjects natural teeth. The twenty subjects (nine men and eleven women with a mean age of 25.5 years, years) were recruited from the school of Stomatology, Wuhan University, China. Two dentists and a dental laboratory technician performed the visual shade matching under northern daylight. The final selected shade was determine either by a majority rule or by consensus when all three shades chosen by the observers differed. The metal ceramic crowns were fabricated by one technician, and the thickness of the opaque, dentin and enamel layers were standardized with a deviation in thickness of 0.05mm. Color measurements of each maxillary left central incisor and the corresponding metal ceramic crowns were measured with a spectroradiometer (PR-650 Spectra Scan, Photo Research Inc., California), illuminated with a D65 light source in a 0/45 degree set up. The color difference between the central incisor and the crown was calculated. The visual assessment method had a mean ΔE value of 3.58 units (1.03 s.d.), which was not significantly different from the instrument method, which had a mean ΔE value of 3.14 units (1.17 s.d.) (Li and Wang 2007). Unlike the study done by Wee et al in 2000, this study used the same porcelain system for all crown porcelain applications, thus allowing comparison of the shade matching methods. However, this study did not report the variation in the color of crowns made of the same shade fabricated by the technician. In summary, studies showed that colorimeters had excellent repeatability while accuracy showed mixed results, with some colorimeters comparable to conventional visual shade matching. Spectrophotometers Spectrophotometers are considered the most accurate instruments for determining color in dentistry because the full spectral power distribution of the light can be measured and analyzed. A modern spectrophotometer has a light source, a diffraction grating or a detector array and a computer processing unit (Berns 2000). In older spectrophotometers, a diffraction grating was used to disperse the light spectrum. Modern spectrophotometers

64 51 use a detector array that measures the amount of reflected light at intervals from 1-25 nm throughout the visible light spectrum. The computer processing unit converts the information into colorimetric coordinates (tristimulus values of XYZ) by multiplying the spectral reflectance data by a CIE standard illuminant factor and a CIE standard observer factor. Spectrophotometers that are designed for tooth shade matching select a shade with the smallest color difference from the instruments database of shade guides. SpectroShade Micro (Medical High Technologies Optic Research, Niederhasli, Switzerland) and Crystaleye (Olympus, Tokyo, Japan) are imaging spectrophotometers that combine a digital camera and a spectrophotometer into a single shade matching system, thus having the benefit of accurate color measurements mapped on a digital image of a photographed tooth. These spectrophotometers can provide a shade match for a whole tooth or any desired area of the tooth, separate shade matches for the cervical, middle and incisal thirds, or detailed shade mapping of the entire tooth. VITA EasyShade Compact (Vita Zahnfabrik, Bad Säckingen, Germany) and Shade-X (X-Rite, Grandville, MI, USA) are spectrophotometers with a small diameter probes that contact the tooth surface allowing measurement of limited areas. In small area color measuring instruments like colorimeters and spectrophotometers, some of the illuminating beam may be scattered within a translucent material beyond the edge of the aperture. The instrument cannot detect any of this light that is refracted out of the material, and therefore error is incorporated in the color measurement. This phenomenon is called edge loss. Edge loss can be minimized by avoiding measurement systems that use a finite opening to position the specimen relative to the illumination and detection components, or by maintaining a large detection area relative to the illumination area. Paul et al compared tooth shades selected for 30 patients by three observers, who did the shade matching visually, with those selected by SpectroShade Micro. For visual matching, a majority rule was applied if at least two of the three observers agreed on the shade and the shade from the spectrophotometer would be chosen if all three shades

65 52 disagreed. For instrumental matching, three consecutive readings were made for each shade determination and the readings were expected to result in the same shade selection in all three readings. Otherwise, the principle of majority as described for the visual matching was applied. The spectrophotometer had a pull down list function that provided ΔE value between the tooth color and any VITA Classical shade selected. The authors used the pull down list function to obtain ΔE values between the shades chosen by each of the three observers and the tooth color. The ΔE values for shades selected by each of the three observers were then averaged. Likewise, the ΔE values between the measured tooth shade and the closest shade provided from the three instrumental measurements were averaged. These investigators found that all three observers agreed on the shade match for only 26.6% of the patients and a majority rule occurred for 46.6% of the patients. With the SpectroShade Micro, all three consecutive readings agreed for 83.3% of the patients and two out of three readings agreed for 100% of the patients. The VITA Classical shades selected by majority rule visual assessment agreed with the shades selected by SpectroShade Micro for 19 out of 30 determinations (63%). The mean color difference between tooth color and the shade tabs using visual assessment (ΔE=3.15 +/- 1.08) were significantly larger (p < ) than the color differences using SpectroShade Micro (ΔE=2.10 +/ ) (Paul et al. 2002). The study concluded that the spectrophotometer was more accurate and more reliable than conventional visual shade matching. A potential bias in this study was that the ΔE values between the tooth color and the visually selected shades were derived from the SpectroShade s database rather than from spectrophotometric measurements made of the shade guide used for the study. In addition, the reported ΔE values between the tooth color and the visually selected shades were averages of the ΔE values for the three observers, while the reported visually selected shade was a single shade determined by majority rule. Using the same methodology, Paul et al did a second study with ten patients. Unlike the first study, the investigators evaluated the shade match of single metal ceramic

66 53 restorations on a maxillary incisor with the adjacent dentition when the restorations were fabricated using a shade selected by either conventional visual shade matching or SpectroShade Micro. For each patient, two metal ceramic crowns were made. The first crown was made according to the shade selected by conventional shade matching method. The second crown was made according to the spectrophotometric measurement, which when completed, was checked with the spectrophotometer in vitro and corrected when necessary. The three observers and the patient visually assessed the crowns intraorally and the crown with the best match was cemented. For conventional visual shade matching, all three observers agreed for 20% of the patients and had a majority agreement for 60% of the patients. In comparison, three SpectroShade Micro measurements agreed for 90% of the patients and had majority agreement for 10% of the patients. There was a 40 % agreement in the VITA Classical shades selected between the conventional visual shade matching method and by SpectroShade Micro. The mean color difference between the teeth and restorations fabricated using visual assessment (ΔE=3.47 +/- 1.84) was significantly larger (p < 0.02) than the color difference using SpectroShade Micro (ΔE=1.91 +/- 0.59). The investigators found that 90% of the crowns shade matched with SpectroShade Micro were preferred over the visually shade matched crowns (Paul et al. 2004). The study concluded that using SpectroShade Micro for shade selection resulted in these crowns being preferred over the visually matched crowns. A potential bias in this study was that spectrophotometrically fabricated crowns had a color quality check (using the spectrophotometer) and color correction step before the final evaluation by the observers and patient. The authors did not report that a check and correction was applied to crowns made using visually selected shades. Derdilopoulou et al compared the repeatability and performance of SpectroShade Micro and conventional shade matching using all anterior teeth from 106 patients on three different occasions. For conventional visual shade matching, two observers independently selected the shade of the patients six maxillary and six mandibular

67 54 anterior teeth using a Chromascop shade guide (Ivoclar Vivadent, Schaan, Liechtenstein) under daylight conditions. For the instrument shade matching, each tooth was measured three consecutive times with the SpectroShade Micro. Each spectrophotometric measurement was matched to the closest Chromascop shade from SpectroShade database based on the smallest color difference, ΔE. The three consecutive shades chosen by SpectroShade Micro agreed for 89.6% of the teeth. The two human observers agreed on the same shade 49.7% of the teeth. An agreement between shades selected by SpectroShade Micro and shades selected by the two observers occurred for 18.2% of the teeth. Spearman s rank correlation test revealed that the two shade selection methods had a positive correlation (ρ=0.548, p < ). The Wilcoxon test was used to determine if there was a difference in the shade selected between the two methods and the test showed the shades determined by visual assessments were significantly darker than shades determined by spectrophotometric assessment (Derdilopoulou et al. 2007). This study showed that the SpectroShade Micro was more repeatable than visual assessments and that SpectroShade Micro recorded lighter shades than visual assessments. Da Silva et al compared the clinical shade matching performance of Crystaleye (Olympus, Tokyo, Japan) and conventional visual shade matching. For each of the 36 subjects, a metal ceramic crown was made based on the visual shade assessment and a metal ceramic crown was made based on the Crystaleye s shade assessment. In the visual shade assessment method, two prosthodontists shade matched each patient s tooth under D65 lighting conditions using the Vitapan 3D-Master (VITA Zahnfabrik), selected as many shades as necessary, drew a tooth shade map, and made descriptive comments as needed. The principal investigator randomly selected one of the two shade matching prescriptions for making the crown. In the Crystaleye shade assessment method, all the software functions and shade matching information were used for the fabrication of the crowns, and color of the fabricated crowns was measured and modified as necessary. The color differences (in ΔE units) between the contra-lateral tooth and each of the two

68 55 crowns were calculated using Crystaleye as a color difference instrument. Three calibrated observers, who were blinded to the method of fabrication, evaluated each crown and gave a score from 1 to 10 (10 = perfect match; 8 = acceptable; 7 = rejected). The observers rated 78% of the crowns made based on the spectrophotometric assessment as acceptable while they rated 22% of crowns made based on the visual assessment as acceptable. In addition, the mean ΔE value between the natural teeth and the crowns made based on the spectrophotometric assessment (2.48, s.d. 1.21) was significantly lower than those that were made based on the visual assessment (4.12, s.d. 1.60) (p < 0.01) (Dasilva et al. 2008). This study showed that crowns fabricated by Crystaleye had a significantly better color match and a lower rate of rejection due to shade mismatch compared to crowns fabricated with visual assessment. Bias could have been introduced in this study by the random selection of one of the two shade matching prescriptions if there was a large difference in ability for the observers to shade match. Another potential bias was that a verification check of the restoration shade was completed for the Crystaleye group, while this is not reported for the visual group. In addition, technicians using Crystaleye had access to digital photographs of the teeth, while they did not for the visual group. Clinically, technicians making crowns based on visually selected shades sometimes have access to high quality photographs of the patients teeth with the visually selected shade tabs next to the teeth. None-the-less, the protocol compares scenarios that may occur clinically. Browning et al compared the shade matching performance of EasyShade to the conventional visual shade matching method, using 95 maxillary anterior teeth in 16 subjects. For conventional visual shade matching method, three calibrated dentists served as observers and shade matched the middle third of the teeth with the VITA 3D Master. For EasyShade shade matching method, a research assistant was trained to make measurements using the EasyShade on all the patients. The probe of the EasyShade was placed perpendicular to the tooth surface in the middle third of the tooth and the unit was

69 56 activated. EasyShade measures the CIE L*, C ab * and h ab values for the tooth and recommends (based on its proprietary software algorithm) the closest matching shade tab. Shade matching by both methods was repeated for all patients in a second session. The study did not report in detail how the dentists were calibrated, whether or not Easy Shade positioning was standardized, or the time interval between the two sessions. The shade matching performance of EasyShade and the conventional visual shade matching method was compared against a standard, which was developed as follows: Each tab from three VITA 3D Master shade guides was measured three times using EasyShade and the average CIE L* values were calculated from the nine values. These shade guides were used exclusively for the study. The CIE L* value was recorded for all 95 anterior teeth with EasyShade and compared to the averaged CIE L* values of the shade guides. The shade that had the least difference in CIE L* was designated as the standard. The same method was applied to obtain the standard in CIE c* range. Using a weighted Kappa statistic, shades selected by EasyShade and the three observers were compared to the standard for the level of agreement in Lightness and Chroma. EasyShade (0.69 for Lightness and 0.92 for Chroma) had higher Kappa values than the observers (range of Kappa from 0.61 to 0.68 for Lightness, and 0.82 to 0.84 for Chroma). For repeatability testing, when comparing shade matches from the first and second sessions, EasyShade had an exact match in 41.4% of the cases, compared with 27.0%, 21.8%, and 16.5% for observers #1, #2, and #3 respectively (Browning et al. 2009). The study concluded that EasyShade was more accurate and repeatable than visual shade matching. However, the use of EasyShade measurement to establish the standards when EasyShade itself was one of the shade matching methods under investigation potentially introduces strong bias in favor of EasyShade. In summary, studies report that spectrophotometers provide a closer shade match of fabricated crowns to the adjacent natural teeth compared to conventional visual shade matching method. However, a major caveat in these studies is that the group of crowns

70 57 made based of the spectrophotometers went through as many shade verification checks and shade corrections as was needed to obtain the best shade match possible while no reporting was made of the group of crowns made based on the conventional visual shade matching method receiving any independent quality control measures in the form of visual assessments by the observers and further shade corrections. In addition, some studies introduced strong bias in the color difference analysis when they used color data of shade guides from the database of the spectrophotometers being tested instead of using the color data of the actual shade guides used in the study (Khurana, et al. 2007). Photographic Theory Dentists are using digital cameras to facilitate communication during the shade matching process. For example, some dentists will take a photograph of the tooth and the shade tab, which they selected through visual shade matching, and send the photograph to the dental laboratory. Color information (RGB values) provided by digital cameras is device-dependent and cannot be used for accurate shade identification. The photo-sensors in different cameras have their own spectral response and sensitivity, which produce different RGB values for the same scene. Although one cannot use the color in the image to identify the shade accurately, the photograph enables the dental technician fabricating the crown to compare the visually matched shade tab with the adjacent teeth. In addition, the photograph provides the technician with information about important characteristics of the tooth, such as translucency, character, opalescence, intrinsic and extrinsic staining. Bengel outlined the following practical guidelines for digital photography to ensure that imaging results pertaining to color rendition are reproducible (Bengel 2003). He stated that the photographer should: 1) take all digital photographs with the same equipment set up and in the same surroundings; 2) set the camera, lens, exposure and camera flash settings to manual mode and keep this set up permanently; 3) use a low ISO

71 58 value of 100 to optimize the light signal to noise ratio; 4) use a custom white balance mode calibrated to a white reference; 5) ensure that the teeth and gingiva are plaque free and saliva free but not desiccated; 6) standardize the camera alignment and focal length (or magnification ratio); 7) use a gray card with a reflectance value of 18% for gray balancing to eliminate color cast and incorrect exposure (Bengel 2003). Wee et al, conducted an in vitro study to determine the accuracy of four color calibration algorithms applied to digital images of 264 color patches and 65 dental shade tabs produced by three different digital camera systems. Using CIELAB values, the investigators compared the transformed color measurements with color measurements made by a radio-spectrophotometer (PR 705, Photo Research Inc., Chatsworth, CA, USA). They found that color measurements from every camera/calibration set up was significantly different from the color measurements made by the spectrophotometer. However, three camera/calibration set ups had mean ΔE values below 2.1 units, suggesting that they might have the potential to produce accurate color matches (Wee, et al. 2006). Several groups have conducted in vitro studies to determine the validity of the digital imaging method for shade matching. Results of these studies have varied. Most research groups studying photographic tooth shade determination have used digital image editing software, such as Photoshop (Adobe Photoshop CS2, Adobe Systems Inc., CA, USA) or Paint Shop Pro, Corel Corp, Ottawa, Canada), to determine the RGB values of the digital image (Jarad, Russell, and Moss 2005; Caglar et al. 2009; Schropp 2009; Lath et al. 2007). Jarad et al and Caglar et al converted the RGB values into CIELAB values using colorimetric converter software such as EasyRGB software (Logicol S.r.l., Trieste, Italy) and Colour Metric Converter software (Color Eng Inc, USA) respectively. Caglar et al compared the color measurements of two composite resin shade guides (Charisma from Heraeus Kulzer, and Premise from Kerr Corporation) and two ceramic shade guides (Vita Lumin Vacuum from VITA Zahnfabrik, and Noritake from

72 59 Noritake Co.) derived from a colorimeter (ShadeEye NCC Dental Chroma Meter, Shofu Inc) and a digital image analyzing method (Adobe Photoshop CS2, Adobe Systems Inc., CA, USA). The digital images were obtained under illuminants of two different color temperatures (2,700K and 6500K), as well as 2,700K and 6,500K combined. Four fluorescent lamps were positioned 15 centimeters away at a 45 degree angle. The first lighting set up used four fluorescent lamps of 2,700K (Philips PL-C 18 W/827), the second set up used four fluorescent lamps of 6,500K (Philips PL-C 18 W/865), and the third set up used two fluorescent lamps of 2,700K and two fluorescent lamps of 6,500K. The fluorescent lamps have a Color Rendering Index (CRI) of 80. The colorimeter had its own illumination using a xenon arc lamp that closely mimics natural daylight (6,500K) with a CRI close to 100. Digital images were made using a digital camera (Fuji S20 Pro, Fujifilm, Tokyo, Japan) and the RGB color values, obtained from Adobe Photoshop CS2, were converted to CIELAB values using EasyRGB software (Logicol S.r.l., Trieste, Italy). The investigators found that all CIELAB values from the digital imaging method were significantly different from the CIELAB values from the colorimeter, except for the CIE a* values under 2,700K-6500K lighting, which were not significantly different from that of the colorimeter. They found a high level of correlation of a* and b* values (R 2 ranged from 0.83 to 0.91) between the colorimeter and the digital imaging method under 2,700-6,500K lighting (Caglar, et al. 2009). No explanation was provided to account for the differences in CIELAB values between the digital imaging method and the colorimeter. These could have resulted from the differences in illumination, digital imaging variables (including the camera), the software conversion from RGB to CIELAB, or variables in the colorimetric measurement. Jarad et al compared conventional visual shade matching method with computerbased visual shade matching method. Ten observers (six dentists and four dental technicians) matched nine arbitrarily selected VITA Classical shade tabs with another VITA Classical shade guide. In the conventional visual shade matching method,

73 60 observers matched test shade tabs that replaced the left maxillary central incisor in complete set of teeth in a phantom head under controlled lighting (6,500K). In the computer matching method, digital images of every test shade tab, positioned in the left maxillary central incisor site, were made using the digital camera (Nikon Coolpix 990). Digital images of every VITA shade tab, positioned in the right central incisor site were also made and the images were cropped to display only the shade tab. Computer software (Adobe Photoshop 5.5) was used to display the image of a test shade tab in left maxillary central incisor position. Digital images of each VITA Classical shade tab were overlaid such that the latter could be moved over the right maxillary central incisor position, allowing the observer to shade match on the computer screen. Each observer had to shade match all nine test shade tabs three times, with the order of the test shades tabs randomized for every shade matching attempt, for both visual and computer matching. The shade matching procedure was repeated on a second session with at least a one-day interval between the sessions. The investigators found that the computer method (61%) had a significantly (p < 0.001) higher percentage of correct matches than the conventional method (43%). For the conventional method, the number of correct shade matches generally increased 10-20% from the first to the second session, while for the computer method, the observers were generally consistent in the number of correct shade matches for the first and second sessions. The investigators noted that the observers varied widely in their matching ability regardless of the method used (Jarad, et al. 2005). Schropp et al did a similar study in which they compared conventional shade matching method with visual shade matching done with digital analogs on a computer screen. Nine observers (four dentists, four dental technicians, and one dental student) had to match twelve arbitrarily selected VITA 3D Master shade tabs with another VITA 3D Master shade guide in a dental clinic with standardized lighting (4,000K). The use of the operatory lamp (4,800K) was permitted if the observer preferred. During shade matching, the test shade tab was positioned in the right maxillary central incisor position in a

74 61 simulator head with a complete artificial dentition. In the computer assisted method, a digital photograph was made of each test shade tab positioned in the simulator head under standardized conditions (Canon EOS 20D, macro lens and flash). The digital photograph was made with all the shade tabs of the VITA 3D Master shade guide arranged above and below the test shade tab. Observers had to use the VITA 3D Master shade tabs in the digital photograph to shade match with the test shade tab within the same digital photograph on a computer screen. The computer assisted method of shade matching was performed approximately two months after the conventional visual shade matching method. The investigators also evaluated the shade matching performance of a computer software (Paint Shop Pro 9.0, Corel Corp, Ottawa, Canada) in which the selected shade match was the shade tab with the smallest color difference (ΔE ) with the test shade tab within the same digital photograph. They found that the conventional visual shade matching method (32% exact matches) and computer assisted visual shade matching method (28% exact matches) were not significantly different (p > 0.05). The shade matching performance of the computer software (67% exact matches) was significantly better (p < 0.02) than the two visual shade matching methods (Schropp 2009). Jarad et al and Schropp et al used the same shade guide system for the test shade tab and the shade guide, which does not represent the clinical situation adequately because the shape, contour, size and color characterization of a natural vital tooth are substantially different from those of shade tabs. Matching shade tabs with shade tabs of the same shade guide system may have resulted in a higher percentage of shade matches compared to shade matching natural teeth. Description of a photographic-based shade matching system Presently, the only commercially available tooth shade analysis system based upon digital photography is ClearMatch (Clarity Dental Corp., Salt Lake City, UT, USA).

75 62 ClearMatch is a color normalizing and shade mapping software program that operates on any Microsoft Windows 32-bit operating system, Windows 98 or higher (Microsoft Corp., Redmond, WA, USA). Developers of ClearMatch (Clarity Dental, Salt Lake City, UT) claim that this software can calibrate and color correct a digital image, and match a tooth shade, regardless of the type of digital camera used. The system uses three points of reference for calibration: a standard white reference, a standard black reference and a known shade tab. All three references must be part of the digital image when the image is captured. The software includes a database of a wide selection of commercially available shade guides that the program compares with the digital image to produce a shade map of the teeth. The dentist can send the digital images by electronic mail to the dental laboratory for shade analysis. Laboratory staff can send the shade map of the tooth back to the dentist to confirm the shade selected. The cost of using this software-based system is significantly lower than the cost of purchasing an intraoral colorimeter or spectrophotometer. The shade map and digital image provide the dental technician more color information and detail in addition to a nominal shade match. To use this technology, the dentist needs to supply a digital camera and a tooth shade guide, in addition to the manufacturer s software and white and black reference tabs. The camera should be able to compose a digital image according to the following manufacturer s recommendations (ClearMatch Clarity Dental Corporation ( )). First, the digital image should capture a macro view of four teeth. Second, light exposures should be consistent, and not too under or over-exposed. Third, a digital camera with a built-in flash centered over the lens or a macro ring flash is recommended. A strong secondary light source such as the dental operatory light should be avoided as it may cause unwanted reflections on the teeth and overpower the light from the flash. Fourth, the digital image must include the white and black color references and a shade tab that visually matches the tooth. While the manufacturer recommends selecting the shade tab that most closely matches the reference tab, some users have suggested that the

76 63 VITA Classical shade A2 is sufficient and they claim to obtain satisfactory results with this method (Ragle; Edmonds). Fifth, the digital image should not show any reflection of the camera flash on the middle third of the teeth and on the color references. Sixth, the digital image should be stored as a JPEG digital image file for subsequent image and shade analysis using ClearMatch. Since this technology was introduced in 2001, no investigators have studied the accuracy and reliability of ClearMatch or the effect of different reference shade tabs on its accuracy. Further, no information is available about how the color database of reference shades was derived. This information is very important because the ability of a shade matching system to identify a tooth shade accurately depends on the quality of the reference data. If this system can accurately and reliably match tooth color, it would be a cost-effective alternative to the other shade matching systems and it could circumvent the problems associated with subjective color perception that occur when humans visually match colors.

77 64 Table 2-1 Laboratory and clinical studies on color acceptability and perceptibility thresholds. Author/date Methodology Color Significance difference Laboratory studies Kuehni trained observers ΔE = 1 Perceptible by 50% of observers Textile/paint specimens Seghi observers ΔE = 2 Perceptible by 100% of observers 31 porcelain discs Ruyter observers ΔE = 3.3 Unacceptable by 50% of observers 13 composite discs Ragain observers ΔE = 2.29 Unacceptable by 50% of observers 6 composite discs Douglas observers ΔE = 1.7 Unacceptable by 50% of observers 60 PFM crowns ΔE = 0.4 Perceptible by 50% of observers Δa* = 1.1 Unacceptable by 50% of observers Δb* = 2.1 Unacceptable by 50% of observers Clinical studies Johnston observers ΔE = 3.7 ± 2.4 Perfect match 42 composite resin veneers ΔE = 6.8 ± 2.7 Marginally acceptable mismatch Douglas observers ΔE = 2.6 Perceptible by 50% of observers 10 shades of interchangeable denture teeth in 1 patient ΔE = 5.5 Unacceptable by 50% of observers

78 65 Table 2-2 Clinical studies that measured tooth color using colorimeters and spectrophotometers. Reference Rubino 1994 Hasegawa 2000 Zhao and Zhu 1998 Dozic 2004 Cho 2007 Gozalo-Diaz 2007 Xiao 2007 Study population 600 Spanish subjects (15-50 years old) 87 Japanese subjects (13-84 years old) 70 Chinese subjects (18-70 years old) 64 Danish subjects (average 32.8 years old) 47 South Korean subjects (average 29 years old) 120 subjects (18-85 years old) 405 Chinese subjects (13-64 years old) Teeth measured Maxillary central incisor Maxillary central incisor 410 maxillary anterior teeth Maxillary central incisor Maxillary and Mandibular Anterior teeth Maxillary central incisor, lateral incisor and canine Maxillary central incisor Measuring method Colorimeter (TOPCON BM-5, Tokyo, Japan) under fluorescent illumination of 4200K. Accuracy of colorimeter verified with Hunter Ultrascan spectrophotometer. Spectroradiometer (PR-650 SpectraColorimeter instrument, PhotoResearch 9330, Chatsworth, California) under xenon illumination of 5500K. Fiber-optic spectrophotometer (FMC- 9204, Kunming, China) under 6500K light source. Digital camera (Camedia C2040ZOOM, Olympus, Tokyo, Japan). Transformed device dependent RGB values to device independent CIELAB values using ITU- R BT.709 compatibility protocol. Tristimulus colorimeter (CM, Chroma Meter CR 321, Minolta, Osaka, Japan) Clinical colorimeter system (SV, ShadeVision System, X-rite, Grandville, MO, USA) Subjects were stratified into 5 age groups with 4 racial categories and balanced for gender. Spectroradiometer (PR 705, Photo Research Inc, Chatsworth, California) under xenon arc lamp illumination Clusters of people from multiple location are randomly chosen Spectroradiometer (PR-650 SpectraColorimeter instrument, PhotoResearch 9330, Chatsworth, California) Mean color coordinates for teeth measured CIE L* 67.6 (s.d. 7.0) CIE a* 4.3 (s.d. 2.1) CIE b* 12.1 (s.d. 3.3) CIE L* 73.1 (s.d. 5.6) CIE a* 3.4 (s.d. 1.2) CIE b* 16.4 (s.d. 3.9) Central incisor CIE L* (s.d. 8.02) CIE a* 0.62 (s.d. 0.14) CIE b* 0.15 (s.d. 0.02) Lateral incisor CIE L* (s.d. 8.44) CIE a* 0.37 (s.d. 0.13) CIE b* 1.96 (s.d. 1.22) Canine CIE L* (s.d. 8.19) CIE a* 1.31 (s.d. 0.57) CIE b* 3.19 (s.d. 0.87) CIE L* 73.8 (s.d. 5.7) CIE a* -1.3 (s.d. 1.4) CIE b* 15.9 (s.d. 3.8) Mean of all teeth measured with: Chroma Meter CIE L* 57.8 (s.d. 3.5) CIE a* -1.0 (s.d. 0.9) CIE b* 6.7 (s.d. 3.1) ShadeVision CIE L* 74.0 (s.d. 3.4) CIE a* 5.0 (s.d.1.5) CIE b* 19.4 (s.d. 4.0) Mean of all teeth measured CIE L* 73.3 (s.d. 7.7) CIE a* 4.7 (s.d. 2.3) CIE b* 18.8 (s.d. 4.9) CIE L* (s.d. 1.91) CIE a* 4.29 (s.d. 2.05) CIE b* (s.d. 4.13)

79 66 CHAPTER 3 MATERIALS AND METHODS General description of the experiment In this experiment a photographic shade analysis system (ClearMatch, Clarity Dental, Salt Lake City, Utah, USA) was compared with conventional visual shade matching using an in vitro test setup to simulate clinical conditions. To model human teeth, 12 target shade tabs were selected from three different shade guides. For the shade matching trials, the target tabs were substituted for the maxillary left central incisor in a mannequin setup. The target shade tabs were shade matched using conventional visual shade matching and using the photographic shade analysis system. Shade matching was completed during two separate sessions three days apart. During each session, three human raters independently determined the best shade match using the VITA Classical shade guide (Vita Zahnfabrik H. Rauter GmbH & Co. KG, Bad Säckingen, Germany). A digital image of each target shade tab was captured using a digital camera and its shade was determined by ClearMatch. Kappa statistics was used to measure the agreement level between shades selected by conventional visual shade matching (VM) and by ClearMatch using VITA Classical A2 as the reference tab (CM-A2), and between shades selected by VM and by ClearMatch using the visually selected shade tab as the reference tab (CMref). Comparing performance of VITA EasyShade and SpectroShade Micro A pilot test was done to determine which of two available intraoral reflectance spectrophotometers, VITA EasyShade (Vita Zahnfabrik H. Rauter GmbH & Co. KG, Bad Säckingen, Germany) or SpectroShade Micro (Medical High Technologies, Zürich, Switzerland), would be used to make the color measurements necessary for the study design and setup. To evaluate the instruments the CIE L*, a* and b* coordinates for all

80 67 shade tabs from an arbitrarily chosen VITA Classical shade guide where measured and compared with published data from previous studies (Table 3-1) (Bayindir, et al. 2007; Paravina, et al. 2007). To simulate the oral environment, color measurements were made with the shade tab placed in the left maxillary central incisor position of a dentoform (Kavo Dentoform Corp., Long Island City, New York, USA). Table 3-2 shows the ΔE, ΔL*, Δa* and Δb* values obtained during the pilot tests for the two instruments and the published data. Lower values signified superior performance by the instruments. The instruments rankings changed depending upon the study and the color coordinate chosen for comparison, but both spectrophotometers performed similarly overall. However, the sum of the mean ΔE values between SpectroShade Micro and the two research studies was lower than the sum for EasyShade, and hence SpectroShade Micro was selected to make all color measurements for the design and setup of this study. To further validate the choice of SpectroShade Micro to make the color measurements necessary for the study design and setup, the repeatability was tested. 10 repeated measurements were made for four selected shade tabs (Table 3-3). To simulate the oral environment, and also aid in positioning the instrument, color measurements were made with the shade tab placed in the left maxillary central incisor position of a dentoform (Kavo Dentoform Corp., Long Island City, New York, USA). Assembly of a representative VITA Classical shade guide for visual matching A single representative shade guide was assembled using shade tabs from 10 individual VITA Classical shade guides, which were obtained from the University of Iowa School of Dentistry. To choose the representative shade guide tabs, CIE L*a*b* values of all shade tabs on each of the guides were measured once with the reflectance spectrophotometer (SpectroShade Micro). To simulate the oral environment, and also aid in positioning the instrument, color measurements were made with the shade tab placed

81 68 in the left maxillary central incisor position of the dentoform (Kavo Dentoform Corp., Long Island City, New York, USA). The mean values for CIE L*, a* and b* were calculated for each of the 16 shades. Color differences, ΔE, were calculated between each shade tab and the corresponding mean value (Table 3-4). For each of the 16 shades of the VITA Classical shade guide, the shade tab with the smallest ΔE to the mean value was selected, and these shade tabs formed the shade guide that would be used for visual shade matching throughout the experiment (Table 3-5). Selection and preparation of target shade tabs To model human teeth, 12 target shade tabs were selected from three shade guides: Trublend SLM (Dentsply International Inc., York, Pennsylvania, USA), Bioform IPN (Dentsply International Inc., York, Pennsylvania, USA) and Portrait IPN (Dentsply International Inc., York, Pennsylvania, USA). Shade tabs were selected from guides other than the VITA Classical guide to simulate clinical conditions in which the tooth being matched and the shade guide reference have different compositions and colors. All tabs chosen were within the color space of the VITA Classical shade guide to ensure that the raters could find potential shade matches on the VITA guide. To select the target teeth, the CIE L*, a* and b* coordinates for the middle third of all shade tabs on the Bioform IPN and Trublend SLM shade guides, and the VITA Classical shade range on the Portrait IPN shade guide were obtained with SpectroShade Micro (Table 3-6). A single color measurement was made of each shade tab positioned in the left maxillary central incisor position of a Dentoform (Columbia Dentoform Corp, Long Island City, New York, USA). Two scatter plots were created of the L*, a* and b* coordinates of the target shade tabs and the representative VITA Classical shade tabs. The first plotted L* on the y-axis versus b* on the x-axis (Figure 3-A), while the second plotted a* on the y-axis versus b* on the x-axis (Figure 3-B). The scatter plots were visualized and 12 target shade tabs

82 69 were selected arbitrarily for use in the experiment. Target tabs where chosen that were spatially close to VITA Classical shade tabs and also well distributed over the shade range (Table 3-7). The clinical shade matching environment An articulated maxillary and mandibular dentoform setup (Kavo Dentoform Corp., Long Island City, NY) was used to simulate the oral cavity. Each dentoform arch was comprised of a hard base covered by a flexible gingival tissue analogue that held the teeth in position. The base of the maxillary dentoform was replaced by a Type 0 vinyl polysiloxane impression material (Exaflex Putty, GC America Inc, Alsip, Illinois, USA). The left maxillary central incisor was replaced by a target shade tab. The putty material was formed within the gingival tissue analogue and around the dentoform tooth roots and the metal handle of the shade tab and allowed to set. The set putty held the dentoform teeth in their original positions and allowed the target shade tabs to be interchanged so that the position and angulation were identical for each test. The metal portion of each target shade tab was shortened so that it was flush with the putty base. The dentoform was magnetically attached to a head simulator with a universal joint in the neck that allowed the head to be positioned. The universal joint was attached to the top of a rectangular plywood sheet, which was strapped to the clinic operatory chair. Visual and photographic shade matching was completed in graduate operatory number 7 in the University of Iowa Faculty and Graduate Prosthodontic Clinic. The operatory walls, ceiling, floor, and dental chair were neutral colors. The clinic used fluorescent lighting (F32T8 Triton 950, Westinghouse Lighting, Philadelphia, Pennsylvania, USA) with a reported color temperature of 5,000 K and a CRI of 98. Light intensity at the head simulator was around 280 lux when measured with a light meter (401025, Extech Instruments Corp, Nashua, New Hampshire, USA). The first session was conducted on Monday, June 6, 2011 from 1230 hrs to 1400 hrs and the second

83 70 session was conducted on Thursday, June 9, 1715 hrs and 1845 hrs in Iowa City, IA, USA. Visual shade matching Three clinicians, each with at least 20 years of clinical experience in shade matching, were selected as raters. A study ID number (1, 2, or 3) was assigned to each rater by drawing lots. The ID numbers were known only to the raters and were used to identify their shade selection choices during the study. Each rater independently determined the closest shade of each target shade tab using the standardized VITA Classical shade guide as the reference. The order in which the target shade tabs were matched was randomized for each shade matching session. The three raters and the investigator were blinded to the target shades throughout the study by covering the shade tab labels and assigning each tab a random four digit ID number (Table 3-8). The shade tab ID number and test order were assigned using the Microsoft Excel software function rand() to generate a random number between zero and one. The raters did not have time limits and they took turns, in no assigned order, assessing the shades. Raters were permitted to adjust the head simulator, the inclination of the operatory chair, and the operatory light. However, they were not allowed to touch the target shade tab. Each rater recorded the shade and his ID number on a piece of paper and placed it in an envelope next to the operatory chair. The investigator collected the envelope once all raters had finished shade matching a target shade tab. The final shade was determined by the principle of majority. If two or three raters selected the same shade, it was designated as the final shade. If, however, all three raters chose different shades, the raters were asked to independently rank the three chosen shades from closest to farthest match. The closest match was given a score of 1, the second closest match was given a score of 2 and the farthest match was given a score of

84 71 3. The scores for each shade were summed and the shade with the smallest overall score was designated as the final shade. To assess repeatability, the visual matching was repeated three days after the first session using the same protocol. The camera set up The camera used for this study was a digital single lens reflex camera (400D, Canon Inc., Tokyo, Japan) with a 100mm macro lens (Canon EF 100mm f/2.8 USM Macro lens, Canon Inc, Tokyo, Japan) and lens-mounted macro ring flash (Canon MR- 14EX, Canon Inc, Tokyo, Japan). Camera settings were as follows: manual mode, shutter speed 1/180, aperture f/27, ISO 200, flash white balance mode. The lens was a set to manual focus and the flash set on manual mode at ¼ flash output. The camera was mounted on a camera arm extension (Shade Matching Arm, Dental Learning Centers, Issaquah, Washington, USA) that standardized the focal distance at 55 cm from the camera to the object. At the object end of the arm extension a jig held the ClearMatch black and white reference plates and two VITA shade tabs. The jig was attached at a 30- degree angle to the object-lens path to minimize the amount of flash reflection on the reference plates and tabs. The camera with the arm extension was positioned so that the long axes of the target shade tab in the dentoform and VITA shade tabs on the arm were parallel. A camera tripod (Tiltall, E.Leitz Inc., Rockleigh, New Jersey, USA), with the camera and arm extension mounted on top, was positioned over the operatory chair such that two legs faced forward while the third leg faced backward. A wooden structural support was specially constructed over the operatory chair to provide a stable base for the tripod (Figure 3-C). Before conducting the shade matching trials, the electronic stability (lack of electronic drift) of the camera and flash set up was tested. Ten consecutive digital photographs of the Portrait D3 target tab were made under the experimental conditions at

85 72 one minute intervals. The digital photographs were analyzed using ClearMatch to determine the primary shade and the percentage of area of the shade tab comprised of the primary shade (Table 3-9). The results from each photograph varied only slightly and the variation was not systematic, indicating that electronic drift was absent. To assess whether the experimental shade matching set up could be accurately reconstructed for repeat measurements on different occasions, the camera tripod, dentoform, and a D3 target shade tab were disassembled and reassembled 10 times. Each time the set up was assembled, a digital photograph was made and analyzed using ClearMatch, as described above (Table 3-10). Results varied only slightly, supporting the conclusion that the experimental conditions could be reconstructed accurately. ClearMatch data acquisition and analysis ClearMatch data acquisition was performed by making digital photographs of the target shade tabs with the described set up. Digital photographs of the target shade tabs were made in the same sequence as that used for the visual shade matching method. Each digital photograph included the target shade tab, the dentoform teeth, the ClearMatch black and white color references, a VITA Classical A2 shade tab, the VITA Classical shade tab that was determined to be the visual match by the three clinicians, and a label showing the test number, target shade tab number (Figure 3-D). The digital photographs were uploaded to a computer (Optiplex 755, Dell, Round Rock, TX, USA) using Microsoft Windows XP operating system (Microsoft Corporation, Redmond, WA, USA) and analyzed with the ClearMatch software program. The manufacturer s default recommendations were used to calibrate the software: the sensitivity setting was set at 2000 and the white and black references were set according to the white and black color reference in each digital image. The reference shade guide was set to VITA Classical. The area of the target shade tab to be analyzed was defined by a rectangle with its sides touching the outline of the crown portion of the tab (Figure 3-

86 73 D). The program created a shade map of the target shade tab and provided the percentage of the area each shade covered. The minimum number of shades on the shade map for the ClearMatch software is two. Of the two shades, the shade with a higher percentage was selected as the final shade match. A screen capture of the original image and the shade map was made and saved for archival purposes (Figure 3-D). To determine the effect of the color of the reference shade tab, this workflow was repeated with the visually matched shade tab substituted for the A2 reference shade tab. To determine the repeatability of the photographic shade matching method, all procedures were repeated three days later. Statistical and Descriptive Analysis Comparisons were made between the shades selected by conventional visual shade matching and shades that were determined by ClearMatch using either VITA Classical A2 (the manufacturer s recommended method) or the visually determined shades for the reference tabs. Comparisons were also made between the shades selected by ClearMatch using VITA Classical A2 as reference and shades selected by ClearMatch using the visually determined shades as reference. In addition, comparisons were made between the initial and repeated measurements for each method. The Kappa statistic was used to measure the level of agreement between the photographic and visual methods and to measure the strength of agreement between the initial and repeated measurements of each method. The closer the Kappa value was to one, the greater the agreement between the two measurements or between the two methods. The following is an approximate guide for interpreting the level of an agreement that corresponds to a Kappa coefficient (Landis and Koch 1977) : 0 = No agreement = Slight agreement = Fair agreement

87 = Moderate agreement = Substantial agreement = Strong agreement 1.00 = Perfect agreement. Agreement in shade matching was further analyzed descriptively by calculating CIE color differences between the shade matching methods and repeat measurments for each method. The magnitude and direction of the overall color difference (ΔE), difference in lightness (ΔL*), difference in green-red component (Δa*), and difference in blue-yellow component (Δb*)were calculated using the following formulas: ΔE (Visual-ClearMatch) = [(ΔL*) 2 + (Δa*) 2 + (Δb*) 2 ] ½ ΔL* (Visual-ClearMatch) = L* (visual) - L* (ClearMatch) Δa* (Visual-ClearMatch) = a* (visual) - a* (ClearMatch) Δb* (Visual-ClearMatch) = b* (visual) - b* (ClearMatch) To complete these calculations, each nominal shade selected by the two shade matching methods was assigned the CIE L*, CIE a* and CIE b* values for the corresponding shade in the representative VITA Classical shade guide, previously determined (Table 3-5). CIE ΔE, ΔL*, Δa* and Δb* values between each shade matching method from the first and second measurements were calculated for all target tabs, and their range and median values were determined. To determine if there were any relationships between color coordinates of the shades selected by the two different methods, Spearman s rank correlation test was used. Because the color coordinates were obtained from the shades of the representative VITA Classical shade guide, they were considered ordinal variables with non-normal distributions. Therefore, Spearman s rank correlation test was performed to evaluate the relationship between color coordinates of the shades selected by the two different methods for the first and second measurements separately. The following is an

88 75 approximate guide for interpreting the strength of the relationship between two variables, based on the absolute value of the Spearman s rank correlation coefficient: ±1.0 = prefect correlation ±0.8 = strong correlation ±0.5 = moderate correlation ±0.2 = weak correlation ±0.0 = no correlation To visualize the relationship of the color coordinates between the different shade matching methods, scatter plots of VM against CM-A2, and VM against CM-ref for CIE L*, CIE a* and CIE b* were generated for the first and second measurements. CIE L*, CIE a* and CIE b* values were presented as ranked numbers as the color coordinates were considered ordinal variables with non-normal distributions. All tests utilized a 0.05 level of statistical significance. Statistical software, SAS for windows version 9.4 (SAS Institute Inc., Cary, North Carolina, USA), was used for all statistical analyses.

89 76 Table 3-1 CIE L*, a* and b* values for VITA Classical shade guide tabs from measurements of an arbitrarily chosen guide using two intraoral spectrophotometers (VITA EasyShade and SpectroShade Micro), and also reported in two previous research studies. (Bayindir, et al. 2007; Paravina, et al. 2007). VITA Classical Shade VITA EasyShade SpectroShade Micro Bayindir et al Paravina et al L* a* b* L* a* b* L* a* b* L* a* b* A A A A A B B B B C C C C D D D

90 Table 3-2 Color differences between measurements of shade tabs from an arbitrarily chosen shade guide (VITA Classical) made by two intraoral spectrophotometers (VITA EasyShade and SpectroShade Micro), and measurements reported in two previous studies (Bayindir, et al. 2007; Paravina, et al. 2007). Shade ES vs Bay ES vs Par Δ E ΔL* Δa* Δb* SS SS ES ES SS SS ES ES SS SS ES ES vs vs vs vs vs vs vs vs vs vs vs vs Bay Par Bay Par Bay Par Bay Par Bay Par Bay Par A A A A A B B B B C C C C D D D Mean SS vs Bay SS vs Par ES EasyShade, SS SpectroShade Micro, Bay Study by Bayindir et al, Par Study by Paravina et al 77

91 78 Table 3-3 Repeated measurements of CIE L*, a* and b*from the middle third of four arbitrarily selected Trublend SLM shade tabs using an intraoral spectrophotometer (SpectroShade Micro). Shade T1 T6 T10 T16 Measurement L* a* b* L* a* b* L* a* b* L* a* b* Mean S.D

92 Table 3-4 CIE L*, a* and b* coordinates of all 16 shade tabs from ten arbitrarily chosen VITA Classical shade guides, and the overall color differences (ΔE) from the mean coordinates for each shade. Color measurements were made of the middle third of the shade tabs using an intraoral spectrophotometer (SpectroShade Micro) (Continues to next page). Guide 1 Guide 2 Guide 3 Guide 4 Guide 5 Shade L* a* b* ΔE L* a* b* ΔE L* a* b* ΔE L* a* b* ΔE L* a* b* ΔE A A A A A B B B B C C C C D D D

93 Table 3-4 CIE L*, a* and b* coordinates of all 16 shade tabs from ten arbitrarily chosen VITA Classical shade guides, and the overall color differences (ΔE) from the mean coordinates for each shade. Color measurements were made of the middle third of the shade tabs using an intraoral spectrophotometer (SpectroShade Micro) (Continued from previous page). Guide 6 Guide 7 Guide 8 Guide 9 Guide 10 Mean Shade L* a* b* ΔE L* a* b* ΔE L* a* b* ΔE L* a* b* ΔE L* a* b* ΔE L* a* b* A A A A A B B B B C C C C D D D

94 81 Table 3-5 CIE L*, CIE a* and CIE b* values of the representative VITA Classical shade guide used for visual matching. Shade L* a* b* A A A A A B B B B C C C C D D D

95 82 Table 3-6 CIE L*, a* and b* values for one arbitrarily chosen Trublend SLM, Bioform IPN and Portrait IPN shade guide, from a single measurement of the middle third of each shade tab using an intraoral spectrophotometer (SpectroShade Micro). Trublend SLM Bioform IPN Portrait IPN Shade L* a* b* Shade L* a* b* Shade L* a* b* T B A T B A T B A T B A T B A T B B T B B T B B T B B T B C T B C T B C T B C T B D T B D T B D T B T B T B T B T B T B T B T B

96 83 Table 3-7 Twelve arbitrarily selected target shade tabs and CIE color differences from the VITA Classical shades that were spatially closest on the scatter plots. Target shade Shade guide Closest VITA Classical shade 1 ΔL* Δa* Δb* ΔE B51 Bioform A B53 Bioform A B65 Bioform A B84 Bioform A A3.5 Portrait A B4 Portrait B C1 Portrait C C3 Portrait C C4 Portrait C D3 Portrait D T5 Trublend C T6 Trublend D The closest VITA Classical shade match was derived from a single reading using SpectroShade Micro

97 84 Table 3-8 Target shade tabs with their corresponding assigned random numbers. Target shade tab Brand Random Number assignment B51 Bioform 6987 B53 Bioform 6829 B65 Bioform 2903 B84 Bioform 9104 A3.5 Portrait 9549 B4 Portrait 9581 C1 Portrait 8612 C3 Portrait 5965 C4 Portrait 5631 D3 Portrait 8041 T5 Trublend 3342 T6 Trublend 9599

98 85 Table 3-9 Repeatability of the camera and flash equipment: ClearMatch shade from 10 consecutive digital photographs of a Portrait D3 shade tab taken at one minute intervals using the experimental setup. Image Number ClearMatch Primary Shade Area of Primary Shade (%) 4428 D D D D D D D D D D3 63.5

99 86 Table 3-10 Repeatability of reconstructing the experimental set up: ClearMatch shade from 10 consecutive digital photographs of a Portrait D3 shade tab taken after disassembly and reassembly of the camera tripod and dentoform setup. Image Number ClearMatch Primary Shade Area of Primary Shade (%) 4442 D D D D D D D D D D3 68.2

100 B1 A1 B51 B2 A2 L* values 70.0 D2 C1 C1 D3 B53 D3 T5 C2 B65 A3 D4 T6 B3 A3.5 A3.5 B4 B C3 C3 A4 B84 C4 C b* values Bioform, Trublend, Portrait VITA Classical Selected shades Figure 3-A Scatter plot of shade tabs from Trublend SLM, Bioform IPN, Portrait IPN, and VITA Classical shade guide, with shades selected for the target tabs indicated.

101 a* values 5 B84 A4 C4 4 A3.5 C4 A3.5 3 A3 B4 D3 C3 B3 B4 D3 C3 2 A2 B65 B53 T5 C2 D4 D2 C1 T6 1 C1 B2 A1 0 B1 B b* values Trublend, Bioform, Portrait VITA Classical Selected shades Figure 3-B Scatter plot of shade tabs from Trublend SLM, Bioform IPN, Portrait IPN, and VITA Classical shade guides, with shades selected for the target shade tabs indicated. 88

102 Figure 3-C Dentoform and camera set up for visual shade matching and recording of the digital photographs 89

103 90 g d a i e b c f h l k j Figure 3-D Computer screen capture of the digital photograph and shade map in the ClearMatch computer program. Digital photograph - Target shade tab with a white rectangle defining the area for shade analysis by ClearMatch (a). Shade tab selected by visual shade matching method (b). A2 shade tab (c). Dentoform teeth (d). White color reference (e). Black color reference (f). Label showing the test number, target shade tab random number, and the shade selected by the visual shade matching method (g). Shade map and software settings Shade map (h). Percentage of the area defined by the white rectangle covered by a certain shade (i). Reference shade guide system (j). Number of shades to match (k). Sensitivity setting (l)