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. 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. Since microaneurysm detection is
decisive in diabetic retinopathy grading, we also tested the proposed method
for this task on the publicly available Messidor database, where a promising
AUC 0.90 with 0.01 uncertainty is achieved in a 'DR/non-DR'-type classification
based on the presence or absence of the microaneurysms.