Automatic Drusen Detection from Digital Retinal Images: AMD Prevention

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1 Automatic Drusen Detection from Digital Retinal Images: AMD Prevention B. Remeseiro, N. Barreira, D. Calvo, M. Ortega and M. G. Penedo VARPA Group, Dept. of Computer Science, Univ. A Coruña (Spain) {bremeseiro, nbarreira, dcalvo, mortega, mgpenedo}@udc.es Abstract. The age-related macular degeneration (AMD) is the main cause of blindness among people over 50 years in developed countries and there are 150 million people affected worlwide. This disease can lead to severe loss central vision and adversely affect the patient s quality of life. The appearance of drusen is associated with the early AMD, so we proposed a top-down methodology to detect drusen in initial stages to prevent AMD. The proposed methodology has several stages where the key issues are the detection and characterization of suspect areas. We test our method with a set of images, obtaining a system with a high sensitivity in the localization of drusen, not just fake injuries. Key words: drusen, AMD, retinal images, template matching, normalized cross correlation, region growing. 1 Introduction The age-related macular degeneration (AMD) [1] is a degenerative eye disease that affects the central vision. This kind of vision is needed to perform daily tasks such as reading, sewing or driving. The AMD causes significant visual impact to the center of the retina, the macula, and therefore the center of the visual field. In 1995, an international classification was proposed by Bird, using color eye fundus images. The AMD was defined as a degenerative disease that affects people over 50 years old and has two stages: early and late AMD. The former is characterized by the presence of drusen and pigment epithelium abnormalities. The later includes the late, atrophic and humid injuries. In this sense, early detection of drusen is useful in the diagnose and treatment of patients that suffer AMD. Therefore, the development of a screening system based on drusen could prevent the AMD. Drusen, in their early stages, are circular, small and white structures which can be observed in retinal images as Fig. 1 shows. There are some works in the literature that try to detect drusen, but none of them is focused on initial stages. For example, the work proposed by Sbeh et al. [2] tries to segment drusen using an adaptative algorithm based on morphological operations. Rapantzikos and Zervakis [3] developed a segmentation technique called HALT (Histogram-based Adaptive Local Thresholding) with the aim of

2 (a) (b) Fig. 1. Examples of drusen: (a) shows a retinal image with two drusen in the macular area and (b) shows the zoom in this area (contrast enhanced). detecting drusen in eye fundus images by extracting useful information. In 2003, Brandon and Hoover [4] proposed a multilevel algorithm to detect drusen in retinal images without human supervision. One year later, the work proposed by Mora et al. [5] uses classical image processing techniques to detect and model drusen. In 2006, Garg et al. [6] proposed two different methods to detect, count and segment drusen in retinal images, without human interaction or supervision. Both of them use morphological characteristics of drusen, such as texture and their 3D profiles. Another work, the proposed by Niemeijer et al. [7], presents a system that allows to detect exudates and cotton-wool spots in color fundus images and distinguish them from drusen. All mentioned works have one thing in common: they detect drusen at any stage, but they do not provide results on their performance in initial stages. However, only drusen detected at early stages can be used to prevent AMD. Thus this work is focused on the automatic detection and characterization of drusen in early stages. We propose a top-down methodology to detect circular diffuse spot with a maximun diameter of 125µm, using techniques such as template matching and region growing. This methodology can be integrated in a screening system for AMD diagnose. This paper is organised as follows: in section 2 a description of the five stages methodology is presented. Section 3 shows the experimental results and validation obtained using a set of retinal images provided by ophthalmologists. Finally, section 4 provides some discussion and conclusion. 2 Methodology The proposed methodology consists of five stages (see Fig. 2). The first stage involves the acquisition of the retinal image. The second stage entails the extraction of the green channel of the colour image. In the third stage, the search area

3 is restricted to the inside of the ETDRS (Early Treatment Diabetic Retinophaty Study) protocol grille. The fourth stage tries to localize the areas of the image which are suspected of being drusen using the template matching technique. Finally, the suspect areas are segmented using the region growing technique and filtered to rule out false lesions. In the following sections, all these stages will be explained in detail. Fig. 2. Methodology general chart 2.1 Acquisition of the Retinal Image The acquisition of the image is the first step towards the drusen detection. All of the images used in this work have been acquired with the F F 450 plus F undus Camera, a 2 Mpx camera. They are colour fundus images, in PPM format and their resolution is pixels. 2.2 Extraction of the Green Channel The green channel of the colour image contains the most of the image information since its contrast is greater than the contrast of the other RGB channels. This is due to the optical characteristics of the eye and the nature of the cameras. the blue channel of the image contains little information while the red channel is too saturated. For this reason, the green channel of the image is extracted and it will be used in next stages. Other works also use the green channel according to the same reasoning [4, 6, 7]. 2.3 Localization of the Search Area The ETDRS [8] is a standard protocol that studies the diabetic retinopathy. The ETDRS protocol grille was initially created to divide the central retina in different areas to the treatment of diabetic people. Nowadays, ophthalmologists use it in other pathologies such as AMD. The drusen outside the grille correspond to a very peripheral area of the vision. In this area, there is neither vision in detail nor colour vision, so the presence of drusen outside the grille has a negligible impact on the visual field. In addition, all of the images are focused on the macula so the peripheral drusen may appear blurred and deformed, distorting, consequently, their analysis. Therefore, the proposed system will detect the drusen inside the grille, so that this area has been called search area. The grille (see Fig. 3) consists of three concentric circumferences focused on the macula. The search area is limited to

4 the area occupied by the grille in order to focus the system on the area of interest. As a result, the drusen are searched in a circumference of 7.2 mm diameter and centered on the macula. The idea proposed by Mariño et al. [9] was used to center the circumference on the macula. Fig. 3. ETDRS protocol grille over a retinal image. 2.4 Detection of the Suspect Areas The detection of the suspect areas is one of the key stages in the proposed methodology. The goal is to identify the regions of the image that might be drusen. It is intended to achieve the fullest possible detection, which means high sensitivity. The technique used is the template matching [10]. Its adaptation to the suspect area problem entails the creation of a template that represents a drusen and the search for parts of the retinography that resemble the template. The similarity measurement used is the normalized cross correlation [10], so the output image will have pixels with values between -1 and 1. A threshold δ is selected to determine which are the suspect areas. Drusen have a circular shape with fuzzy edges and a whitish colour. Their intensity is variable, but always higher than the surrounding retinal tissue. Regarding the size, we only consider those drusen with a maximum diameter of 125µm. Due to the drusen characteristics, two different templates were tested: circular templates and gaussian templates. As drusen have different sizes, a multiscale approach was used. The experimental results obtained with four test images proved that the most suitable configuration includes two gaussian templates with radius 3 and 4 and square window sizes 9 and 15, respectively. The threshold was set to δ = Figure 4 shows the results which were obtained in a retinal image after applying this stage, using the above-mentioned parameters.

5 Fig. 4. Results after the detection of the suspect areas. Two drusen were detected (upper circle), which means 100% sensitivity, and one false positive was included in the set of suspect areas (lower circle). 2.5 Characterization of the Suspect Areas In the previous stage, all the suspect areas, this is, the candidate areas to contain drusen, were identified. The goal was to get a high sensitivity despite of the number of false positives. In this stage, the areas previously detected are analyzed to determine if they are drusen. This way, the number of false positives is reduced. This stage has two important steps: the segmentation of the suspect areas, to achieve a good fit of the candidate regions, and the region filtering, to reduce the number of false positives. The goal of the segmentation process is to distinguish the different regions the suspect areas contain. In order to achieve a good fit of the candidate areas, the technique used is region growing [11]. This technique involves three steps: the selection of the center of mass or seed associated with each region, the definition of a criterion to include a pixel in a region and, finally, the creation of a stopping criterion to finish the segmentation. In our case, the seed for each suspect area is the point of maximum correlation for each region: R i, S i = p j /corr(p j ) = max{corr(p k ), p k R i }, i = 1... N. (1) where R i is the i th region of the N suspect areas of this stage, S i is the seed of the R i region and p j is the j th pixel of the R i which correlation value is the maximum of this region. Moreover, a pixel is added to a region if it exceeds a threshold ϑ and is neighbor of another pixel that belongs to that region. Since lighting is not constant throughout the retina this threshold is computed for each suspect area using the next equation:

6 ϑ(x, y) = I bg (x, y) α(i bg (x, y) I(x, y)). (2) where I is the input image, I bg is the input image after applying a median filter and α is a weighting variable, with values between 0 and 1. In this work α = 0.6. The process finishes when no more pixels can be added to any existing region or if the region exceeds the maximum size ζ = 150 pixels. After the segmentation process, we have a vector which contains all the candidate areas. This vector is processed to analyze the candidate areas and do the region filtering process. In this case, four properties were studied to reduce the number of false positives: size, circularity, intensity and correlation mean. The first two do not work because the segmented structures are very tiny. Also, the third one does not work due to the high variability in the tonality of the images. This way, the correlation mean is analyzed in order to rule out false lesions from the suspect areas. The idea is to create a correlation mean filter to eliminate the candidates which pixels do not show continuity with respect to their correlation value. The average of the correlation values of the pixels in each region is computed as follows: R i, ν(r i ) = 1 m m corr(p j ), p j R i, i = 1... N. (3) j=1 where R i is the i th region of the N segmented areas, ν(r i ) is the correlation average value of R i and p j is the j th pixel of the m pixels belonging to this group. Then, the candidates which average value does not exceed a threshold ϱ are eliminated. We have set ϱ = 0.35 after several experiments with four test images. Figure 5 shows the results which were obtained in the same retinal image than in the previous stage, after applying the segmentation process and the region filtering process, using the above-mentioned parameters. 3 Results The proposed methodology was tested with a set of images in PPM format obtained with a F F 450 plus F undus Camera. Two different experiments have been used to prove the accuracy of the proposed methodology. The first experiment consists of two test benchs. The first bench has four images with 11 drusen in initial stages marked by ophtalmologists and the second one has five healthy retinal images. We had obtained a sensitivity of 82% whereas the number of false positives is close to 0 (see Table 1). The second experiment arises due to the problem to evaluate the method, because of the shortage of images showing drusen in incipient stages. This experiment consists of two test benchs too. The first bench has four images with 11 drusen in initial stages and 13 drusen added artificially. The second bench has five healthy retinal images with 16 drusen added artificially. The method to add

7 Fig. 5. Results after characterizing the suspect areas: two drusen detected (inside the circle), which means 100% sensitiviy, and no false positive. Bench 1 Bench 2 TP FN FP Average FP Sensitivity Average FP % 0.6 Table 1. Final results for the proposed methodology in the first experiment drusen artificially is based on inserting real drusen in retinal images by means of a cloning process. The same process is used by specialists. We had obtained results similar to the previous experiment (see Table 2). Bench 1 Artificial drusen Real drusen Average FP Sensitivity % Bench 2 Artificial drusen Real drusen Average FP Sensitivity % Table 2. Final results for the proposed methodology in the second experiment These results can not be compared with previous work since there is no previous work devoted to detect drusen in initial stages. Also, it is too difficult to do a thorough testing process because of the shortage of images. Anyway, we have got a high sensitivity, more than 82%, and the number of false positives is practically zero.

8 4 Conclusions and Future Research In this work a method for the detection of drusen in initial stages has been presented, to support ophthalmologists in the prevention of the AMD. This method does a first detection of suspect areas and a later classification of them. We have developed a system that is able to automatically detect drusen in retinal images with a high sensitivity (over 80%) and without hardly detecting false lesions. The proposed system could be integrated into a screening system to prevent the AMD. This system could be improved in several ways. First, new drusen properties could be used to create new filters to eliminate spurious injuries. Furthermore, we could use aditional information by means of other kind of images, such as OCT images. It would be very important to create a database with images containing drusen marked by specialists in order to do a more exhaustive testing process. References 1. : Age-Related Macular Degeneration (AMD), National Eye Institute. Website 2. Sbeh, Z.B., Cohen, L.D., Mimoun, G., Coscas, G., Soubrane, G.: An adaptive contrast method for segmentation of drusen. In: ICIP 97: Proceedings of the 1997 International Conference on Image Processing (ICIP 97) 3-Volume Set-Volume 1, Washington, DC, USA, IEEE Computer Society (1997) Rapantzikos, K., Zervakis, M.: Nonlinear enhancement and segmentation Algorithm for the Detection of Age-related Macular Degeneration (AMD) in Human Eye s Retina, Proceedings of ICIP 2001, Thessaloniki, Greece, October 2001 (2001) 4. Brandon, L., Hoover, A.: Drusen Detection in a Retinal Image Using Multi-level Analysis. In: MICCAI (1). (2003) Mora, A., Vieira, P., Fonseca, J.: Drusen Deposits on Retina Images: Detection and Modeling. In: MEDSIP-2004, Malta (2004) 6. Garg, S., Sivasway, J., Joshi, G.D.: Automatic Drusen Detection from Colour Retinal Images. In: Proc. of Indian Conference on Medical Informatics and Telemedicine (ICMIT), Kharagpur (2006) Niemeijer, M., van Ginneken, B., Russel, S., Suttorp-Schulten, M., Abramoff, M.: Automated detection and differentiation of Drusen, exudates, and cotton-wool spots in digital color fundus photographs for early diagnosis of Diabetic Retinopathy. Investigative Ophthalmology & Visual Science 48 (2007) : Early Treatment Diabetic Retinopathy Study (ETDRS), National Eye Institute. Website 9. Mariño, C., Pena, S., Penedo, M.G., Rouco, J., Barja, J.M.: Macula precise localization using digital retinal angiographies. In: ICCOMP 07: Proceedings of the 11th WSEAS International Conference on Computers, Stevens Point, Wisconsin, USA, World Scientific and Engineering Academy and Society (WSEAS) (2007) Russ, J.C.: The image processing handbook (3rd ed.). CRC Press, Inc., Boca Raton, FL, USA (1999) 11. González, R., Woods, R.: Digital image processing. (1992)