DIABETIC RETINOPATHY (DR) is a serious eye disease that originates from diabetes mellitus and is the most common cause of blindness in the developed countries. Microaneurysms (MAs) are early signs of this disease, so the detection of these lesions is essential in the screening process. Reliable microaneurysm detection in digital fundus images is still an open issue in medical image rocessing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first, and also on two other databases. We use an effective MA detector based on the combination of preprocessing methods and candidate extractors.
