CHAPTER 8 EVALUATION OF FUNDUS IMAGE ANALYSIS SYSTEM Diabetic retinopathy is very common retinal disease associated with diabetes. Efforts to prevent diabetic retinopathy though have yielded some results; it has been still one of the most serious diseases causing vision loss in the working age population in many countries. Early detection of diabetic retinopathy through regular screening is particularly important to prevent vision loss. CAD technology and digital retinal imaging will help to aid the large scale screening of people having diabetes. This thesis focuses on the development of a fundus image analysis system. This system selects the images that include signs of occurrence of diabetic retinopathy and present only these images to an ophthalmologist. To distinguish normal fundus images from images having diabetic retinopathy, the fundus image analysis system must be able to identify any type of abnormality associated with diabetic retinopathy with high sensitivity. The most important signs of diabetic retinopathy are dark lesions (i.e. microaneurysms and hemorrhages) and bright lesions (i.e. exudates and cottonwool spots). Determination of the position of the normal anatomical landmarks such as the blood vessel network, the optic disk and the fovea is also important. These anatomical structures provide reference co-ordinates for identifying the abnormalities on the retina. Additionally the system must deal
with images having variations in colour, illumination levels and amount of noise. In this thesis several screening system components are developed: blood vessel segmentation (Chapter 3), localization and contour detection of optic disk (Chapter 4), fovea detection (Chapter 5), bright lesion detection (Chapter 6) and dark lesion detection (Chapter 7). In this Chapter these components are combined to form a complete fundus image analysis system that can be employed for diabetic retinopathy screening. The system starts by automatically segmenting the vasculature, detecting the location of optic disk and fovea. Next, red and bright lesions are detected in the fundus image. The proposed fundus image analysis system grades diabetic retinopathy and macular edema based on the detection of these lesions and it also provides the spatial distribution of abnormalities centered on fovea such that an ophthalmologist can make a detailed diagnosis. The performance of the fundus image analysis system is compared with three diabetic retinopathy screening systems [113-115]. This chapter is organized as follows. In Section 8.1, the proposed fundus image analysis system is presented. Experimental results are presented and compared to several existing diabetic retinopathy screening systems in Section 8.2. Conclusions are given in Section 8.3.
8.1. FUNDUS IMAGE ANALYSIS SYSTEM The proposed fundus image analysis system consists of six integral components as shown in Fig. 1.5. First, the blood vessel segmentation component extracts the binary segmentation of vasculature from the digital fundus images using proposed HMLRE method. The output of this component is used by the optic disk detection, fovea detection and dark lesion detection components. The optic disk detection component finds the location of optic disk based on finding the vessel branch with maximum vessel connections. A geometric active contour method is proposed to detect the contour of the optic disk. The location of optic disk is used in fovea detection and bright lesion detection components. In bright lesion detection component, the location of optic disk is used to remove any spurious bright lesion detections on the optic disk. Its diameter is used to localize fovea. The fovea detection component detects vascular arcade, macula and fovea. Based on the location of fovea a fundal coordinate system is set up that is used to find the severity of diabetic retinopathy. The output of this component is used for diagnosis of diabetic retinopathy component. SWFCM based clustering method is developed for bright lesion detection component to detect exudates and cottonwool spots. The dark lesion detection component uses a newly developed hybrid detection method to detect dark lesions such as microaneurysms and hemorrhages. The outputs of these two components are used by diagnosis of diabetic retinopathy component. The diagnosis of diabetic
retinopathy component is designed to provide four grading levels for diabetic retinopathy and three levels for macular edema. 8.1.1. Grading Diabetic Retinopathy The FIA system is designed to provide four grading levels of diabetic retinopathy according to MESSIDOR database [116]. Level-0: No diabetic retinopathy (Normal): (Dark lesions = 0) AND (Bright Lesions = 0) Level-1: Mild diabetic retinopathy: (0 < MA <= 5) AND (HM = 0) AND (Bright Lesions = 0). Level-2: Moderate diabetic retinopathy: ((5 < MA < 15) OR (0 < HM < 5)) AND (Bright Lesions = 0). Level-3: Severe diabetic retinopathy: (MA >= 15) OR (HM >=5) OR (NV = 1) OR Bright Lesions. MA: Number of Microaneurysms; HM: Number of Hemorrhages; NV = 1: Neovascularization; NV = 0: no neovascularization. 8.1.2. Grading Macular Edema Bright lesions are employed to grade the risk of macular edema Level-0 (No risk): No visible bright lesions. Level-1: Shortest distance between macula and bright lesions > one papilla diameter. Level-2: Shortest distance between macula and bright lesions <= one papilla diameter.
After grading diabetic retinopathy and macular edema, this component establishes a polar fundal coordinate system centered on fovea based on the ETDRS Report-10 to divide the retinal image into 10 sub-regions to study the spatial distribution of red and bright lesions. This helps the ophthalmologists for better understanding of the severity of diabetic retinopathy. 8.2. EXPERIMENTAL RESULTS AND DISCUSSION A dataset of 1540 images is used for evaluating the proposed fundus image analysis system. The images obtained from diverse sources have a lot of disparities in colour, illumination and quality. The images considered for this work from various sources are as follows: 81 images are taken from the STARE database [55], 130 images are from the DIARETDB0 database [101], 89 images are from the DIARETDB1 database [102], 1200 are from the MESSIDOR database [116] and 40 images are from the DRIVE database [94]. The images in these databases are classified as normal and abnormal on the basis of the presence and absence of the lesions. The STARE database consists of 30 normal images and 51 abnormal images. The DIARETDB0 database comprises 20 normal and 110 abnormal fundus images. Out of 89 images in DIARETDB1 database, 34 are normal and 55 are abnormal. The DRIVE database includes 33 normal and 7 abnormal images. The MESSIDOR database has 1200 images in which 540 are normal and the other 660 being abnormal. All the 1540 images of the dataset are employed on the
proposed fundus image analysis system. In these 1540 images, 631 images are identified to have no lesions by ophthalmologists, while the lesions are present in other 909 images. The presence of lesions is successfully detected by the fundus image analysis system in all the 909 images with a sensitivity of 100% and specificity of 96.98%. The sensitivity and specificity for diabetic retinopathy system [113] are 88.5% and 99.1% respectively on a database of 30 images. Diabetic retinopathy system developed by Usher et al. [114] has sensitivity of 94.8% and specificity of 52.8% on 1406 image database. The sensitivity and specificity for the diabetic retinopathy system by Nagayoshi et al.[115] are 96.1% and 81% on 223 image database. The fundus image analysis system developed in this thesis is tested on a large database compared to other systems and attained better results with high sensitivity and specificity. The other aspects of the fundus image analysis system are: it grades the diabetic retinopathy and gives the location and area of lesions which are not defined in existing diabetic retinopathy screening systems. Tables 8.2 and 8.3 represent the grading of diabetic retinopathy and grading of macular edema by the fundus image analysis system. The outputs of the fundus image analysis system on a fundus image from DIARETDB1 database is shown in Figs.8.1(a) and (b). The locations of bright lesions and dark lesions in these two fundus images are given in Tables 8.4 and 8.5 respectively.
Table 8.1. Performance Comparison of the Fundus Image Analysis System with other Diabetic Retinopathy Screening Systems Diabetic Retinopathy System Number of Images in the Dataset Sensitivity Specificity Sinthiyothin et al. [113 ] 30 88.5% 99.1% Usher et al. [114 ] 1406 94.8% 82.8% Nagayoshi et al. [115 ] 223 96.1% 81% Fundus Image Analysis System 1540 100% 92.98% Table 8.2. Grading of diabetic retinopathy by Fundus Image Analysis System on the Dataset Diabetic Retinopathy Grade Number of Images Sensitivity Specificity Level-0 631 100% 92.98% Level-1 232 96.85% 95.71% Level-2 282 98.58% 96.29% Level-3 395 97.05%, 96.42% Table 8.3. Grading of Macular Edema by Fundus Image Analysis System on the Dataset Macular Edema Grade Number of Images Sensitivity Specificity Level-0 923 100% 89.46% Level-1 462 98.55% 90.10% Level-2 155 98.63% 91.94%
(a) (b) Fig.8.1. Results proposed Fundus Image Analysis System on an Image from DIARETDB1 Database. (a) Detected Bright Lesions with Established Fundal Coordinate System (b) Detected Dark lesions with Established Fundal Coordinate System Region Table 8.4. Location of Bright Lesions in Fig. 8.1(a) Central Inner Superior Nasal Inferior Temporal (Pixels) 74 60 131 19 522 Region Outer Far Superior Nasal Inferior Temporal Temporal (Pixels) Total 576 Absence 10 225 12 1629 pixels Table 8.5. Location of Dark lesions in Fig. 8.1(b) Region Central Inner Superior Nasal Inferior Temporal (Pixels) 18 175 3 158 157 Region Outer Far Superior Nasal Inferior Temporal Temporal (Pixels) 51 36 971 1052 44 Total 2665 pixels
8.4. CONCLUSIONS In this chapter a fundus image analysis system is developed based on the components discussed in previous chapters. The system starts by automatically detecting the anatomical structures of the retina: blood vessel network, optic disc and fovea. Then, it identifies abnormalities like hard exudates, cottonwool spots, hemorrhages and microaneurysms present in the retina. The fundus image analysis system grades diabetic retinopathy and Macular Edema based on the detection of these lesions and it also provides the spatial distribution of abnormalities based on fovea such that an ophthalmologist can make a detailed diagnosis. This system presents encouraging results in identifying and grading images having diabetic retinopathy. The proposed fundus image analysis system performs better in identifying fundus images with diabetic retinopathy compared to other recently developed diabetic retinopathy screening systems with a sensitivity of 100% and specificity of 96.98%. As the proposed fundus image analysis system achieved high sensitivity and reasonable specificity, it can be used to assist ophthalmologists in the screening and treatment of diabetic retinopathy.