Detection and classification of diabetic retinopathy using K-means clustering and fuzzy logic

Abstract

Diabetic retinopathy is a common vision threatening disease which occurs due to the abnormalities in retina of diabetic patients. Detection of diabetic retinopathy is crucial for prevention of loss of vision and diagnosis of diabetic. As fundus are sensitive to vascular syndromes, efficient detection of diabetic retinopathy related exudates and hemorrhages in fundus images of the retina can be a helpful for effective screening. This study presents an effective approach to detect exudates and hemorrhages in retinal fundus image and classify the diabetic retinopathy with reasonable cost. A k-means color compression technique is used to cluster the fundus image in different region of interest reducing color dimension. The different parts of diabetic fundus then segmented out and analyzed through the region properties attributes. Finally, the recognition of diabetic retinopathy was done by the knowledge based fuzzy inference system (FIS) with these effective attributes through experiment and trail basis. The sensitivity and accuracy of the detector is found as 98.2% and 92.3% respectively. The HRF retinal fundus image database images are successfully classified by fuzzy logic classifier with accuracy up to 96.67%.