The presence of microaneurysms in the eye is one of the early signs of diabetic retinopathy, which is one of the leading
causes of vision loss. We have been investigating a computerized method for the detection of microaneurysms on retinal
fundus images, which were obtained from the Retinopathy Online Challenge (ROC) database. The ROC provides 50
training cases, in which "gold standard" locations of microaneurysms are provided, and 50 test cases without the gold
standard locations. In this study, the computerized scheme was developed by using the training cases. Although the
results for the test cases are also included, this paper mainly discusses the results for the training cases because the
standard" for the test cases is not known. 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. Twelve image features were determined, and
the candidate lesions were classified into microaneurysms or false positives using the rule-based method and an artificial
neural network. The true positive fraction of the proposed method was 0.45 at 27 false positives per image. Forty-two
percent of microaneurysms in the 50 training cases were considered invisible by the consensus of two co-investigators.
When the method was evaluated for visible microaneurysms, the sensitivity for detecting microaneurysms was 65% at
27 false positives per image. Our computerized detection scheme could be improved for helping ophthalmologists in the
early diagnosis of diabetic retinopathy.