@inproceedings{f4624245f6d144f7957a1d5fb3616bfc,
title = "MRMR optimized classification for automatic glaucoma diagnosis",
abstract = "Min-Redundancy Max-Relevance (mRMR) is a feature selection methodology based on information theory. We explore the mRMR principle for automatic glaucoma diagnosis. Optimal candidate feature sets are acquired from a composition of clinical screening data and retinal fundus image data. An mRMR optimized classifier is further trained using the candidate feature sets to find the optimized classifier. We tested the proposed methodology on eye records of 650 subjects collected from Singapore Eye Research Institute. The experimental results demonstrate that the new classifier is much compact by using less than of the initial feature set. The ranked feature set also enables the clinicians to better access the diagnostic process of the algorithm. The work is a further step towards the advancement of the automatic glaucoma diagnosis.",
author = "Zhuo Zhang and Kwoh, {Chee Keong} and Jiang Liu and Fengshou Yin and Adrianto Wirawan and Carol Cheung and Mani Baskaran and Tin Aung and Wong, {Tien Yin}",
note = "Copyright: Copyright 2012 Elsevier B.V., All rights reserved.; 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 ; Conference date: 30-08-2011 Through 03-09-2011",
year = "2011",
doi = "10.1109/IEMBS.2011.6091538",
language = "English",
isbn = "9781424441211",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
pages = "6228--6231",
booktitle = "33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011",
}