Automatic exudate extraction for early detection of Diabetic Retinopathy

Diabetic Retinopathy (DR) is the most common cause of blindness in diabetic patients, but early detection and timely treatment can prevent this problem. Exudates have been found to be one of the signs and serious DR anomalies so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. The aim of this study is to automatically detect these lesions in fundus images. To achieve this goal, the proposed method first preprocesses to improve the quality of fundus image, and then Optic Disc (OD) is detected and eliminated to prevent the interference to the result of exudate detection by combination of 3 methods; image binarization, Region Of Interest (ROI) based segmentation and Morphological Reconstruction (MR). Next, exudates are detected by applying the maximum entropy thresholding to filter out the bright pixels from the result of OD region eliminated. Since the result contains some noises which appear as bright light at the edge of fundus area in some images, that affect is considered and eliminated to improve the result of false positive. Finally, exudates are extracted by using MR. The proposed technique has been tested on 100 fundus images from hospital. Experimental results show that 91 % of exudate is extracted correctly with the average process of 3.92 second per image.