Automated microaneurysm detection method based on double-ring filter and feature analysis in retinal fundus images

Microaneurysm in the retina is one of the signs of simple diabetic retinopathy. We have been investigating a computerized method for the detection of microaneurysms on retinal fundus images. In this study, the computerized scheme was developed by using twenty five cases. After image preprocessing, candidate regions for microaneurysms were detected using a double-ring filter. Any potential false positives located in the regions corresponding to blood vessels were removed by automatic extraction of blood vessels from the images. One hundred twenty six image features were determined, and 28 components were selected by using principal component analysis, and the candidate lesions were classified into microaneurysms or false positives using the rule-based method and an artificial neural network. The true positive rate of the proposed method was 68% at 15 false positives per image.