Automatic Detection of Hard Exudates in Diabetic Retinopathy Using Morphological Segmentation and Fuzzy Logic

Retinal image analysis is an essential step in the diagnosis of various eye diseases. Diabetic Retinopathy (DR) is globally the primary cause of visual impairment and blindness in diabetic patients. Early diagnosis through regular screening and timely treatment has proven beneficial in preventing visual impairment and blindness. In this paper we have proposed a novel approach to automatically detect diabetic retinopathy from digital fundus images. The digital fundus images are segmented employing morphological operations to identify the regions showing signs of diabetic retinopathy such as hard exudates, soft exudates and the red lesions: microaneurysm and haemorrhages. Various color space values of the segmented regions are calculated. A fuzzy set is formed with the color space values and fuzzy rules are derived based on fuzzy logic reasoning for the detection of diabetic retinopathy. Experimental evaluation on the publicly available dataset DIARETDB0 demonstrates the improved performance of the proposed approach in the diagnosis of diabetic retinopathy.