Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods

Authors:
BáLint Antal;AndráS Hajdu
Affiliations:
University of Debrecen, Faculty of Informatics, 4010 Debrecen, POB 12, Hungary;University of Debrecen, Faculty of Informatics, 4010 Debrecen, POB 12, Hungary
Venue:
Pattern Recognition
Year:
2012

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Abstract

In this paper, we present an approach to improve microaneurysm detection in digital color fundus images. Instead of following the standard process which considers preprocessing, candidate extraction and classification, we propose a novel approach that combines several preprocessing methods and candidate extractors before the classification step. We ensure high flexibility by using a modular model and a simulated annealing-based search algorithm to find the optimal combination. Our experimental results show that the proposed method outperforms the current state-of-the-art individual microaneurysm candidate extractors.