An optimal voting scheme for microaneurysm candidate extractors using simulated annealing

In this paper, we present a novel approach to improve microaneurysm candidate extraction in color fundus images. The individual algorithms published so far can be hardly considered in an automatic screening system. To improve further the sensitivity, specificity and image classification rate of microaneurysm detection, we propose an appropriate combination of individual algorithms. Thus, we investigate the detection of microaneurysms through the following phases: first, we use different approaches to extract microaneurysm candidates. Then, we select candidates voted by a sufficient number of the candidate extractor algorithms. The optimal number of votes and participating algorithms are determined by a simulated annealing algorithm. Finally, we classify the candidates with a machine-learning based approach by following the current literature recommendations. Our framework improves the positive likelihood ratio for the microaneurysms and outperforms both the state-of-the-art individual candidate extractors and microaneurysm detectors in these terms.