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

Guillaume Lemaitre 1 Mojdeh Rastgoo 1 Joan Massich 1 Carol Y. Cheung 2 Tien Y Wong 2 Ecosse Lamoureux 2 Dan Milea 2 Fabrice Mériaudeau 3, 1 Désiré Sidibé 1
1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6303]
2 Singapore Eye Research Institute, Singapore National Eye Centre
3 CISIR - Centre for Intelligent Signal and Imaging Research (Universiti Teknologi Petronas)
Abstract : This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (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 pre-processings in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and non-linear 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 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 pre-processing is inconsistent with respect to different classifiers and feature configurations.
Keywords : Diabetic Macular Edema Optical Coherence Tomography DME OCT LBP
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Contributeur : Guillaume Lemaitre <>
Soumis le : mardi 24 mai 2016 - 14:08:43
Dernière modification le : mercredi 12 septembre 2018 - 01:27:41

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Guillaume Lemaitre, 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. Journal of Ophthalmology, Hindawi Publishing Corporation, 2016, 2016. ⟨hal-01320791⟩

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