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DETECTION OF DIABETIC RETINOPATHY USING RADIAL BASIS FUNCTION - praneeth - 10-04-2017 DETECTION OF DIABETIC RETINOPATHY USING RADIAL BASIS FUNCTION [attachment=15967] I. INTRODUCTION Diabetic Retinopathy (DR) cause blindness [1]. The prevalence of retinopathy varies with the age of onset of diabetes and the duration of the disease . Color fundus images are used by ophthalmologists to study eye diseases like diabetic retinopathy [2]. Big blood clots called hemorrhages are found. Hard exudates are yellow lipid deposits which appear as bright yellow lesions. The bright circular region from where the blood vessels emanate is called the optic disk. The fovea defines the center of the retina, and is the region of highest visual acuity. The spatial distribution of exudates and microaneurysms and hemorrhages[3], especially in relation to the fovea can be used to determine the severity of diabetic retinopathy II. MATERIALS AND METHODS This research work proposes contextual clustering (CC) and Radial Basis Function (RBF) network. CC is used for feature extraction. The extracted features are input to the RBF network. In order to achieve maximum percentage of identification of the exudates, proper data input for RBF, optimum topology of RBF and correct training of RBF with suitable parameters is a must. A large amount of exudates and non exudates images are collected. Features are extracted from the images using contextual clustering segmentation. The features are input to the RBF and labeling is given in the output layer of RBF. The labeling indicates the exudates. The final weights obtained after training the RBF is used to identify the exudates. Figure 1 explains the overall sequence of proposed methodology. II.CONTEXTUAL CLUSTERING Image segmentation is a subjective and context-dependent cognitive process. It implicitly includes not only the detection and localization but also the delineation of the activated region. In medical imaging field, the precise and computerized delineation of anatomic structures from image data sequences is still an open problem. Countless methods have been developed, but as a rule, user interaction cannot be negated or the method is said to be robust only for unique kinds of images. |