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

Guillaume Lemaître (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 [UMR6306]
2 Singapore Eye Research Institute, Singapore National Eye Centre
3 CISIR - Centre for Intelligent Signal and Imaging Research [Petronas]

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.

Mots clés: Diabetic Macular Edema; Optical Coherence Tomography; DME; OCT; LBP

Domaines: Traitement du signal et de l'image [eess.SP]; Imagerie médicale; Vision par ordinateur et reconnaissance de formes [cs.CV]

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. Journal of Ophthalmology, 2016, 2016. ⟨hal-01320791⟩