Automated Microaneurysms Detection and Classification using Multilevel Thresholding and Multilayer Perceptron

Abstract

The purpose of this paper is to propose an automatic detection of microaneurysms (MAs) in the fundus retina images. In this work, E-optha database of 100 images were utilised to test the performance of the proposed method. The approach covers pre-processing, segmentation, post-processing, feature extraction and classification phases. In pre-processing, the images were filtered and the contrast enhanced. Then, the images were segmented using H-maxima and thresholding technique. Morphological operation was carried out to enhance the images before feature extraction and MAs candidate detection. The detected MAs candidates were classified using multilayer perceptron (MLP). After that, the detected MAs were classified into three classes including background (B), MAs and retinal blood vessels (RBVs). The performances of the classifiers were evaluated in terms of accuracy, sensitivity and specificity. The MLP classifier achieved a better performance than the support vector machine with the highest accuracy of 92.28% under condition 2. This study demonstrated a methodology for automatic detection of MAs using MLP. The proposed methodology successfully classify the MAs, B and RBVs and was reasonably fast to be implemented in real time.