An ensemble-based microaneurysm detector for retinal images

The key to the early detection of diabetic retinopathy is to recognize microaneurysms in the fundus of the eye in time. Reliable detection of such lesions is still an open issue in medical image processing. We propose a framework which assembles several candidate extractors and preprocessing methods to strengthen the detection accuracy of the individual approaches. We use a simulated annealing based search approach to select an optimal combination from the available methods. The proposed method has proved its superiority over the individual algorithms in an online competition dedicated to the objective comparison of microaneurysm detectors.