A Computationally Efficient and Integrated Approach for the detection of exudates in color fundus images

Exudates are one of the earliest and most prevalent symptoms of Diabetic Retinopathy(DR), which is a serious complication of diabetes mellitus and a major cause of blindness worldwide. Certain areas of the retina with such conditions are to be photocoagulated by laser to stop the disease progress and prevent blindness. Outlining these areas is dependent on outlining the exudates, the blood vessels, the optic disc and the macula and the region between them. The earlier the detection of exudates in fundus images, the stronger the kept sight level. So, early detection of exudates in fundus images is of great importance for early diagnosis and proper treatment. In this paper, a robust and computationally efficient approach for the localization of the different features and lesions in a fundus retinal image is presented. Since many features have common intensity properties, geometric features and correlations are used to distinguish between them. First, the blood vessels are removed based on Mathematical Morphology and the exudates are segmented by using column wise neighborhood filter. We proposed a new constraint for optic disk detection by using circular fitting method based on brightest point. After filtering the optic disc, the borders are removed to detect the exudates. We also show that many of the features such as the blood vessels, exudates and micro aneurysms and hemorrhages can be detected quite accurately using this technique. The performance of the proposed system is carried out on DRIVE Database.