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Journal of Ophthalmology
Volume 2016 (2016), Article ID 3298606, 14 pages
http://dx.doi.org/10.1155/2016/3298606
Research Article

Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection

1LE2I UMR6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, 71200 Le Creusot, France
2Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Eye Hospital, 147K Argyle Street, Kowloon, Hong Kong
3Singapore Eye Research Institute, Singapore National Eye Center, Singapore
4Electrical & Electronic Engineering Department, Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi Petronas, Tronoh, 32610 Seri Iskandar, Perak, Malaysia

Received 27 November 2015; Revised 15 February 2016; Accepted 24 May 2016

Academic Editor: Theodore Leng

Copyright © 2016 Guillaume Lemaître et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with DME versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various preprocessing steps in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and nonlinear classifiers, we tested the developed framework on a balanced cohort of 32 patients. Experimental results show that the proposed method outperforms the previous studies by achieving a Sensitivity (SE) and a Specificity (SP) of 81.2% and 93.7%, respectively. Our study concludes that the 3D features and high-level representation of 2D features using patches achieve the best results. However, the effects of preprocessing are inconsistent with different classifiers and feature configurations.