Authors:
Bálint Antal
;
István Lázár
and
András Hajdu
Affiliation:
University of Debrecen, Hungary
Keyword(s):
Biomedical image processing, Image classification, Pattern recognition, Medical decision-making, Statistics.
Related
Ontology
Subjects/Areas/Topics:
Design and Implementation of Signal Processing Systems
;
Image and Video Processing, Compression and Segmentation
;
Multimedia
;
Multimedia Signal Processing
;
Multimedia Systems and Applications
;
Neural Networks, Spiking Systems, Genetic Algorithms and Fuzzy Logic
;
Telecommunications
Abstract:
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 individua
l candidate extractors and microaneurysm detectors in these terms.
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