1M. Tech student,
2Associate Professor,
Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. 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. Since microaneurysm detection is decisivein diabetic retinopathy grading, we also tested the proposed method for this task on the publicly available Messidor database.