Red Small Dot Segmentation for Early Warning of Diabetic Retinopathy

Abstract

Diabetic retinopathy is a medical condition occurs to the retina due to diabetes mellitus and leading cause of blindness. Early detection of diabetic retinopathy is very necessary. Therefore, an automated system to detect abnormalities in retinal fundus image is expected to avoid further damage to the retina. In this study, we proposed an automated system to detect red small dots in retinal fundus images. The method is the combination of Tyler Coye and morphological supremum of opening algorithm to enhance blood vessel segmentation for red small dot detection. The system consists of three parts, the first part is the process to segment the dark area of retinal fundus image after eliminating bright regions including optic disc and exudates. The second part is the process to segment the blood vessel, the third part is to obtain the red small dot segmentation by subtracting the result of the first with the second part. The test performance evaluated using accuracy, sensitivity, and specificity. The result is 99.69%, 70.88% and 99.73% respectively.