Tsallis Entropy Segmentation and Weighted KNN Classifier-Based Automatic DR Detection from Retinal Fundus Images
Retina of individuals suffering from diabetes mellitus for several years signifies characteristic group of lesions of diabetic retinopathy (DR). DR is an asymptotic disease and emerging as one of the primary cause of vision loss in developing world. Ophthalmologist uses the retinal fundus image for manual screening of DR. To overcome the limitations of manual screening like highly subjective, time-consuming, prone to error, and requirement of expert availability, automated techniques are developed. We propose automated DR detection algorithm which uses retinal fundus images and detects exudates as sign of DR. Initially, the fundus image is preprocessed using Gaussian filter to remove the image noise. The preprocessing is followed by segmentation of fundus image. Optic disc is segmented using morphological operation and eliminated. The exudates which are prime sign of DR are segmented using Tsallis entropy segmentation and classified using weighted KNN. Various performance measures of the proposed technique are evaluated and compared with the existing exudate detection algorithms.
KeywordsDiabetic retinopathy Exudate Tsallis entropy w-KNN classifier
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