EFFICIENT DETECTION OF MICRO-ANEURYSMS IN RETINAL IMAGES FOR EARLY DIAGNOSIS OF DIABETIC RETINOPATHY

Abstract

Diabetic retinopathy (DR) is a serious complication of diabetes mellitus and one of the major causes of blindness worldwide. As the number of diabetic patients increases, early detection of DR for regular screening can prevent loss of vision and blindness. The development of algorithms for detecting dark lesions may turn very useful in the early diagnosis and screening of retinopathy diseases. In this paper, a novel hybrid algorithm for the microaneurysms (MAs) detection is developed. This task is based on mathematical morphology, which is followed by a classification step. Although some algorithms have been developed, the accurate detection of MAs in color retinal images is still a challenging problem. The proposed approach aims to increase the number of true positives and minimize the false positives compared with methods developed in the literature. The new approach is tested on a set of 219 ophthalmologic images. A total of 12 microaneurysm features are considered in this study and selected for KNN classification. The validity of detection process is checked through comparisons at the pixel level with ophthalmologists’ hand-drawn ground truth. Sensitivity, specificity, prediction rate, and accuracy achieved by the proposed approach are 98.13%, 99.71%, 99.63% and 99.01% respectively.