A Survey on Microaneurysms Detection in Color Fundus Images

Abstract

Early Detection of Microaneurysms (MA) plays a vital role in preventing the blindness caused by diabetic retinopathy (DR). DR is preventable yet a serious diabetic problem. Treatment at an earlier stage reduces the risk of blindness. Microaneurysm is the first sign of DR found in fundus images while doing screening. Detection of MA is a challenging task mainly because of its size. MA appears as a tiny red spot ranging from 15µm to 60µm size. The most common way to detect the MA from a colour fundus image is by classification/segmentation through machine learning and deep learning approaches. The FROC-based performance evaluation shows that the existing methods can reach only up to 80% of sensitivity at 8 False Positive per Image on average. In recent researches, machine learning and deep learning approaches are equally competing each other to be better in detecting MA both in lesion level as well as pixel-level.