Towards Vessel Characterisation in the Vicinity of the Optic Disc in Digital Retinal Images

Transcription

1 Towards Vessel Characterisation in the Vicinity of the Optic Disc in Digital Retinal Images H.F. Jelinek 1, C. Depardieu 2, C. Lucas 2, D.J. Cornforth 3, W. Huang 4 and M.J. Cree 4 1 School of Community Health, Charles Sturt University, Australia 2 Department of Information Technology, University of Poitiers, France 3 School of Information Technology and Electrical Engineering, UNSW@ADFA, Canberra, ACT, Australia 4 Department of Physics and Electronic Engineering, University of Waikato, Hamilton hjelinek@csu.edu.au Abstract Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel patterns in the retina. This paper describes progress towards the development of an integrated automated analyser of the retinal blood vessels in the vicinity of the optic disc using digital colour retinal images. First the optic disc was detected using a combination of Butterworth filtering, canny edge detection and morphological filters. After finding initial points using a median filter, blood vessels were tracked at one optic disc radius from the optic disc boundary, by two-dimensional fitting of a physically inspired model to a local region of a vessel. The last step involved the classification of the segmented vessels into arteries and veins, by using colour and hue features as inputs to a variety of classifier algorithms. The optic disc was located to within 2.5% of one optic-disc diameter in 13 of 20 images when compared to the manually identified optic disc centre. Using the median filter we obtained good accuracy for locating blood vessels. Optimisation of the blood vessel classifier using the naïve Bayes rule, resulted in a mean accuracy of 70% (s.d.=17.6%) over eight images analysed. Keywords: retinal imaging, optic disc detection, blood vessel classification, diabetic retinopathy, 1 Introduction Diabetic retinopathy (DR) is globally the primary cause of blindness not because it has the highest incidence but because it often remains undetected until severe vision loss occurs. Diabetic retinopathy is characterised by changes in the retina that include blood vessel diameter changes, microaneurysms, lipid and protein deposits referred to as hard exudates and cotton wool spots depending on the appearance, haemorrhages and new vessel growth [1, 2]. These pathological changes are known risk factors for severe vision loss, hypertension and cardiovascular disease [3]. Provided the disease is detected early treatment is effective at reducing eye-sight loss. Advances in shape analysis, and the development of strategies for the detection and quantitative characterisation of blood vessel changes in the retina, are therefore of great clinical importance. Direct digital image acquisition using fundus cameras combined with powerful image processing and analysis techniques has the potential to enable automated diabetic retinopathy screening. Automated screening for diabetes associated eye pathology is of benefit for several reasons. Most important of these is that pathology can be detected at the asymptomatic stage of disease progression and is amendable to treatment with good outcomes. In addition the availability of digital imaging and advanced computer pattern analysis methods provide the possibility of diabetic retinopathy screening to be utilised by primary health care professionals in rural and remote areas where specialist health care is lacking. Normal features of the retinal fundus include the optic disc, the macula and the blood vessels. This paper describes the segmentation of the optic disc and blood vessels in order to identify arteries and veins. The aim is to develop an automated system that can chacterise blood vessels in the vicinity of the optic disc. 2 Related Literature We are not aware of any completely automated system capable of locating the optic disc, detecting the blood vessels and making useful measurements such as the ateriolar-venule diameter ratios in the vicinity of the optic disc, however a semi-automatic system that requires substantial operator intervention is described by Li et al. [4]. In the following we review the literature for each of the independent steps necessary for the complete system, namely optic disc detection, segmentation of blood vessels,

2 classification of blood vessels as arterioles and venules and measurement of vessel diameter. 2.1 Optic Disc Detection The optic disc is the brightest object in the healthy retinal fundus and several algorithms have been described to locate its centre and boundary [5-8]. These authors reported the use of Canny edge detection, template matching and Haar transforms. Optic disc detection however remains a problematic task due to the discontinuities along the boundary where blood vessels cross, as well as dramatic hue changes within the optic disc boundary, with the most extreme being due to intra-disc haemorrhage. Pathologies, such as exudate rings, also confound some approaches. The most effective method has been reported by Osareh based on the active-contour model or snakes, however the methodology is quite complex, and has a long computation time.[9]. An equally good method is that of Abdel-Ghafara [8]. The above approaches are all based on finding the optic disc by its distinctive shape and colour. It is also possible to locate the optic disc by tracking vessels back to their origin, as all vessels emerge into the retina via the optic disc. This approach has been taken by [10, 11], however one has to detect a good portion of the blood vessel network first in this approach. 2.2 Blood Vessel Segmentation The literature on blood vessel segmentation, even just in retinal images, is extensive and we cannot do justice to it here. Broadly, there are two approaches to vessel segmentation. The first is the `pixel processing approach which works by applying global image processing operators, such as morphological or adaptive filters, to segment the vessels, followed by post-processing such as thinning, or artificial intelligence methods to improve reliability. Reviews of such approaches can be found in [12] and comparative studies have been performed by [13, 14]. The second approach is by vessel tracking or tracing. An initial point on a vessel is required and then the vessel is tracked by exploiting local image properties. These algorithms, since they only need analyse images in the vicinity of vessels, are fast and scale well to varying image resolution. For these reasons we adopted this approach. 2.3 Blood Vessel Identification This area of automated classification of retinal blood vessels has not received much attention in the literature, although methods of delineating blood vessels into arteries and veins are currently used by various groups.[4, 10] Most often colour (RGB mean and standard deviation) and hue (HSV mean and standard deviation) information is used for classifying retinal blood vessels into arteries and veins. The availability of toolboxes for performing a variety of statistical and machine learning methods make this an accessible approach. 3 Methods We used twenty images obtained by a Canon CR5 non-mydriatic camera with a digital 6.3 megapixel back for analysis. All images were transformed into their red plane for optic disc boundary detection and green plane for blood vessel tracking and classification. 3.1 Optic Disc Boundary Detection The red plane image was first reduced in size using bilinear interpolation for efficiency. The image was then normalised using a high pass Butterworth filter of order 0.5 and width D 0 = 10 by filtering in the Fourier domain [15]. The local intensity standard deviation filter was then used to locate the optic disc as the region of greatest variation. The filter defines a neighbourhood around each pixel of interest and calculates the standard deviation for the neighbourhood to determine the pixel value in the output image. We then applied a greyscale morphological closing with a flat, disc-shaped structuring element to remove the edge of the blood vessels. Canny edge detection is then applied with a low threshold of 0.4 and a high threshold of 0.1 and the Gaussian standard deviation at 4. A morphological closing with a disc structuring element is applied to close any gaps in the edge of the perimeter of the optic disc. A flood fill operator then fills the region of the optic disc and a morphological opening removes any area under 1200 pixels. A morphological dilation is used to trim any spurious lines and the object is reduced to its perimeter. 3.2 Segmentation of Blood Vessels The vessel tracking algorithm employed is described in a companion paper [16] and requires four parameters to be initialised: the x and y coordinates of the starting point, the width and the orientation of the vessel. As shown in Fig. 1, three circles have been drawn around the optic disc. The vessels need to be tracked in Zone A, which is located between half and one optic disc diameter from the optic disc boundary. The vessel tracking is started from the middle circle outwards to the outer circle. The starting points for vessel tracking are determined on the middle circle. First, we extract the intensity profile along the middle circle and calculate the local medians for each pixel. The starting points for the tracking are located by identifying pixels that correspond to the minimum intensity between a dropping and a rising crossover along the median values. In this way, we also estimate the width of a vessel as the distance between the corresponding two

3 crossover points. If this width is below a preset threshold, the corresponding starting point is eliminated. The starting orientation of a vessel is set at this stage from the centre of the optic disc to the starting point. Occasionally this fails to initiate vessel tracking and orientations 15 degrees either side of the orientation are then tried. 4 Results 4.1 Optic Disc Boundary Detection Optic disc segmentation using morphological operators and canny edge detection performed rather well. Of 20 images, seven produced poor results. However, as shown by the example in Figure 2, the other images provided very good optic disc detection with an error in localisation of 2.5% of an optic disc diameter. Figure 1: Region of Interest for blood vessel classification. Vessel tracking from the starting points is performed using 2D non-linear least squares fitting. The fitting algorithm models the cross-sectional intensity profile of a blood vessel as double Gaussian curve, which is a combination of two Gaussian models [4]. The first Gaussian is a representation of the spread of the vessel whereas the second Gaussian represents the central reflex. It is caused by light reflection at the back of the vessel. Because it is brighter than its background, the curve is simply an inverse. A benefit of the tracking algorithm is that it provides accurate vessel diameter measurements at regular intervals along the vessel as part of the tracking process. 3.3 Blood Vessel Classification Retinal images suffer quite often from inhomogeneity in luminosity, contrast and hue, both within the same image as well as between images. In order to compensate for this variability we employed a previously developed normalisation algorithm on our images [17]. Ten to twelve vessel segments were selected from three images representing veins and arteries. Eight features were measured from each segment for automated classification: mean and standard deviation for red, blue, green and hue. From these we obtained a list of records, each record corresponding to a vessel segment, along with the values for these eight features. The Weka toolbox was used for automated classification, with various classifier algorithms and the leave-one-out paradigm [18, 19]. The most successful classifiers and features were subsequently applied for discriminating veins from arteries. Using the records from already labelled vessel segments to train the classifier algorithm, we then obtained results on previously unlabelled images. Figure 2: An example showing segmentation of the optic disc using a morphological operator and canny edge detection. 4.2 Vessel Tracking The vessel tracking method used was successful, with the majority of vessels within the region of interest correctly detected. Figure 3 illustrates the intensity profile and the median filter outcome from one of the images studied. The upper line represents the actual intensity of pixels at points around the circumference of a circle at twice the optic disk radius from the disk centre. The smoother line represents the local intensity median. Note that the actual intensity crosses the median line at several places around the circle. Red crosses are minima and indicate a possible starting point for vessel tracking. Figure 3: Detection of vessels crossing the circle about the optic disc. The red crosses indicate the detected centres of vessels. An example of the final outcome is shown in Figure 4. This result was obtained using the green plane and the double Gaussian procedure. Note that the majority of vessels were automatically segmented

4 the three classifiers on unknown images. These results are shown in Table 1. Figure 4: Blood vessel identification and tracking. 4.3 Blood Vessel Classification Eight images were selected for the classification of blood vessels. First we determined the optimal features to extract, using the Correlation-based Feature Subset Evaluation method of Weka [20]. This provides a relative ranking of the suitability of each feature for discrimination. Results, averaged across eight images, are shown in Figure 5 Figure 6: Illustrating the relative success of each classifier. Figure 5: Relative success of each feature for classification. The figure above represents the participation of each attribute to provide a good classification. The best attribute is the Green mean (89%). The hue mean and standard deviation contribute 20% and 13% respectively. The red and blue values do not contribute significantly. We tested 13 classifiers available through the Weka toolbox on 8 images during the training phase. For each classifier and each image, we obtained the number of correct class predictions. Figure 6 represents the mean for each classifier. The three best classifiers are naïve-bayes (92%), DecisionTable (91%) and J48 (92%). These classifiers were used for the analysis of new images with previously unknown class labels (artery or vein). After obtaining unlabelled records comprising mean and standard deviation measures as before, these images were labelled using previously trained classifiers. The resulting classified vessel segments were then assessed by experts to discover an accuracy figure for Figure 7: Final Classification result: a) original image, with optic disk clearly visible, b) image after normalisation, c) extracted vessels, d) original image with classified vessel segment superimposed. Table 1: Results for machine classification of blood vessels. Image NaiveBayes DecisionTable J % 100% 100.0% % 50% 50.0% % 88% 87.5% % 50% 50.0% % 63% 62.5% % 63% 62.5% % 88% 75.0% % 63% 62.5% MEAN 70% 70% 69%

5 In order to illustrate the steps taken, we provide results from a typical image in Figure 7. Notice that although not all vessel segments were detected, the results are sufficient to provide measures for the assessment of diabetic retinopathy, such as the arteriolar-venule diameter ratios 5 Discussion Our automated diabetic retinopathy screening tool has several novel features. Foremost of these is that it attempts to provide a comprehensive interpretation of the retinal fundus from asymptomatic changes to severe retinopathy. Thus identifying arteriolar-venous changes may occur several years before onset of diabetes and also is a strong risk indicator for risk of hypertension, heart attack and stroke [21]. To identify the region of interest for blood vessel classification we need to first locate the optic disc boundary. In our application we used several well known morphological operators and combined these in a novel way with the Butterworth filter and canny edge detection to segment the boundary of the optic disc. Our results were surprising given the simplicity of the procedure, with an error of less than 2.5% that could be improved still further. Remaining errors are most probably due to background in-homogeneity and may be improved by applying an algorithm which analyses the retinal background area to detect changes of luminosity and contrast, and through an estimation of their local statistical properties derives a compensation for their drifts [22]. We described a method for initiating tracking from the optic disc. Most vessels were successfully identified and investigations are continuing to identify the reason why some failed to track. The tracking algorithm can provide diameter measurements, making AVR calculations straightforward. Blood vessel detection provided good results with the simplest decision rule and mainly the red colour mean as the feature. Little significance should be attached to small differences between algorithms when classifying blood vessels in this study, as the relatively small dataset will result in some noise in these figures. Most of the methods tried have a fairly high success rate, while NaiveMultinominal and IBK3 have perhaps lower performance. This is a little surprising, as these are more sophisticated than some of the other methods. However, the relatively high performance of all these methods gives us confidence that an automated method can indeed distinguish the vessel types. We expect further improvement in these results with more careful selection of features. 6 Conclusion In this paper we have presented for the first time an integrated, fully automated approach to obtaining labelled vessels in the vicinity of the optic disk. This approach includes optic disk detection, vessel segmentation and vessel classification. Our preliminary results presented here are encouraging, given the relative simplicity of this method, and imply that with some fine tuning, the method has the potential to achieve an even higher accuracy. This method would significantly enhance the diagnostic tools available to practitioners of diabetic screening, especially in rural and remote areas. 7 Acknowledgements HJ was in receipt of a Charles Sturt University Grant and a Diabetes Australia Society (DART) grant. Ophthalmic images were provided by Dr Tien Wong and technical assistance by Irene Tam. 8 References [1] NHMRC, "National Health and Medical Research Council. Management of diabetic retinopathy clinical practice guidelines." Australian Government Publishing Service, Canberra [2] R. Klein, B. E. Klein, S. E. Moss, T. Y. Wong, L. Hubbard, K. J. Cruickshanks, and M. Palta, "The relation of retinal vessel caliber to the incidence and progression of diabetic retinopathy: XIX: the Wisconsin Epidemiologic Study of Diabetic Retinopathy," Archives of Ophthalmology, vol. 122, pp , [3] T. Y. Wong, R. Klein, B. E. Klein, J. M. Tielsch, L. Hubbard, and F. J. Nieto, "Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality," Survey of Ophthalmology, vol. 46, pp , [4] H. Li, W. Hsu, M. L. Lee, and T. Y. Wong, "Automated grading of retinal vessel caliber," IEEE Transactions on Biomedical Engineering, vol. 52, pp , [5] A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, "Classification and localisation of diabetic-related eye disease," presented at ECCV2002, [6] C. Sinthanayothin, J. F. Boyce, H. Cook, and T. Williamson, "Automated localisation of the optic disc, fovea and retinal blood vessels from digital colour fundus images.," British Journal of Ophthalmology, vol. 83, pp , [7] B. M. Ege, O. K. Hejlesen, O. V. Larsen, K. Møller, B. Jennings, D. Kerr, and D. A. Cavan, "Screening for diabetic retinopaqthy using computer based image analysis and statistical classification," Computer Methods

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