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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. Ourmethod 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.
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01430668
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : mardi 10 janvier 2017 - 10:35:57
Dernière modification le : lundi 10 août 2020 - 16:28:02

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Guillaume Lemaître, Mojdeh Rastgoo, Joan Massich, Carol Y. Cheung, Tien Y. Wong, et al.. Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection. American Journal of Ophthalmology, Elsevier Masson, 2016, 2016, pp.1 - 14. ⟨10.1155/2016/3298606⟩. ⟨hal-01430668⟩

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