%0 Journal Article %1 ANTAL2012264 %A Antal, Bálint %A Hajdu, András %D 2012 %J Pattern Recognition %K retinopathy %N 1 %P 264 - 270 %R https://doi.org/10.1016/j.patcog.2011.06.010 %T Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods %U http://www.sciencedirect.com/science/article/pii/S0031320311002780 %V 45 %X 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.

@article{ANTAL2012264, 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.}, added-at = {2019-11-11T10:33:26.000+0100}, author = {Antal, Bálint and Hajdu, András}, biburl = {https://www.bibsonomy.org/bibtex/21260df898c6139edd3d86ab27fe9098a/gnoyel}, description = {Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods - ScienceDirect}, doi = {https://doi.org/10.1016/j.patcog.2011.06.010}, interhash = {f4cd45ddd37f773c355a5298191e159e}, intrahash = {1260df898c6139edd3d86ab27fe9098a}, issn = {0031-3203}, journal = {Pattern Recognition}, keywords = {retinopathy}, number = 1, pages = {264 - 270}, timestamp = {2019-11-11T10:33:26.000+0100}, title = {Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods}, url = {http://www.sciencedirect.com/science/article/pii/S0031320311002780}, volume = 45, year = 2012 }