Automated Microaneurysm Detection in Fundus Images through Region Growing

Abstract

Diabetic retinopathy (DR) is the leading cause of blindness if not detected and treated in time and is a serious complication of diabetes. Since DR is a progressive eye disease, the early detection and diagnosis of DR is important to prevent patients from blindness. One of the most characteristic symptoms of DR is the presence of microaneurysm (MA) – the early sign of DR, which is hard to detect manually due to its small size. In this paper, we propose an automatic MA detection method based on region growing and region classification. We solve two problems: 1) given a fundus image, how to automatically partition the image into regions that may or may not contain MAs through a region growing approach, and 2) given a region in a fundus image, how to automatically evaluate whether this region contains MA by feeding the features of the region into an artificial neural network (ANN) for classification. The proposed approach involves image preprocessing, region growing, feature selection and classification steps. In the experiment, the public dataset DIAbetic RETinopathy DataBase 1 (DIARETDB1) is used to provide training/testing data and ground truth. The proposed method can achieve the performance with sensitivity 86.6%, specificity 96.3%, and accuracy 93.9%, for automatic MA detection.