Identification and classification of microaneurysms for early detection of diabetic retinopathy

Diabetic retinopathy is a progressive eye disease which may cause blindness if not detected and treated in time. The early detection and diagnosis of diabetic retinopathy is important to protect the patient's vision. The accurate detection of microaneurysms (MAs) is a critical step for early detection of diabetic retinopathy because they appear as the first sign of disease. In this paper, we propose a three-stage system for early detection of MAs using filter banks. In the first stage, the system extracts all possible candidate regions for MAs present in retinal image. In order to classify a candidate region as MA or non-MA, the system formulates a feature vector for each region depending upon certain properties, i.e. shape, color, intensity and statistics. We present a hybrid classifier which combines the Gaussian mixture model (GMM), support vector machine (SVM) and an extension of multimodel mediod based modeling approach in an ensemble to improve the accuracy of classification. The proposed system is evaluated using publicly available retinal image databases and achieved higher accuracy which is better than previously published methods.

► Accurate identification of microaneurysms for early detection of diabetic retinopathy. ► Shape, color, gray level and statistical feature set for classification. ► The paper presents a novel hybrid classifier for improved accuracy. ► Achieved high sensitivity, specificity, positive prediction and accuracy values. ► Detailed comparison with previous techniques.