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Proceedings Paper

Glaucoma detection based on local binary patterns in fundus photographs
Author(s): Maya Alsheh Ali; Thomas Hurtut; Timothée Faucon; Farida Cheriet
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Paper Abstract

Glaucoma, a group of diseases that lead to optic neuropathy, is one of the most common reasons for blindness worldwide. Glaucoma rarely causes symptoms until the later stages of the disease. Early detection of glaucoma is very important to prevent visual loss since optic nerve damages cannot be reversed. To detect glaucoma, purely data-driven techniques have advantages, especially when the disease characteristics are complex and when precise image-based measurements are difficult to obtain. In this paper, we present our preliminary study for glaucoma detection using an automatic method based on local texture features extracted from fundus photographs. It implements the completed modeling of Local Binary Patterns to capture representative texture features from the whole image. A local region is represented by three operators: its central pixel (LBPC) and its local differences as two complementary components, the sign (which is the classical LBP) and the magnitude (LBPM). An image texture is finally described by both the distribution of LBP and the joint-distribution of LBPM and LBPC. Our images are then classified using a nearest-neighbor method with a leave-one-out validation strategy. On a sample set of 41 fundus images (13 glaucomatous, 28 non-glaucomatous), our method achieves 95:1% success rate with a specificity of 92:3% and a sensitivity of 96:4%. This study proposes a reproducible glaucoma detection process that could be used in a low-priced medical screening, thus avoiding the inter-experts variability issue.

Paper Details

Date Published: 18 March 2014
PDF: 7 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903531 (18 March 2014); doi: 10.1117/12.2043098
Show Author Affiliations
Maya Alsheh Ali, Univ. Paris Descartes (France)
École Polytechnique de Montréal (Canada)
Thomas Hurtut, Univ. Paris Descartes (France)
École Polytechnique de Montréal (Canada)
Timothée Faucon, DIAGNOS inc. (Canada)
Farida Cheriet, Ecole Polytechnique de Montréal (Canada)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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