title: Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection.
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.
authors.affiliation: LE2I UMR6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, 71200 Le Creusot, France.
authors.forename: Guillaume
authors.identifier: 0000-0002-0897-6791
authors.initials: G
authors.lastname: Lemaître
authors.affiliation: LE2I UMR6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, 71200 Le Creusot, France.
authors.forename: Mojdeh
authors.identifier: 0000-0003-4696-7797
authors.initials: M
authors.lastname: Rastgoo
authors.affiliation: LE2I UMR6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, 71200 Le Creusot, France.
authors.forename: Joan
authors.identifier: 0000-0002-0111-3088
authors.initials: J
authors.lastname: Massich
authors.affiliation: Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Eye Hospital, 147K Argyle Street, Kowloon, Hong Kong.
authors.forename: Carol Y
authors.initials: CY
authors.lastname: Cheung
authors.affiliation: Singapore Eye Research Institute, Singapore National Eye Center, Singapore.
authors.forename: Tien Y
authors.initials: TY
authors.lastname: Wong
authors.affiliation: Singapore Eye Research Institute, Singapore National Eye Center, Singapore.
authors.forename: Ecosse
authors.initials: E
authors.lastname: Lamoureux
authors.affiliation: Singapore Eye Research Institute, Singapore National Eye Center, Singapore.
authors.forename: Dan
authors.initials: D
authors.lastname: Milea
authors.affiliation: LE2I UMR6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, 71200 Le Creusot, France; Electrical & Electronic Engineering Department, Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi Petronas, Tronoh, 32610 Seri Iskandar, Perak, Malaysia.
authors.forename: Fabrice
authors.initials: F
authors.lastname: Mériaudeau
authors.affiliation: LE2I UMR6306, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, 71200 Le Creusot, France.
authors.forename: Désiré
authors.initials: D
authors.lastname: Sidibé