Classification of SD-OCT Volumes with LBP: Application to DME Detection

Guillaume Lemaître (1, 2) , Mojdeh Rastgoo (2, 1) , Joan Massich (2) , Shrinivasan Sankar (2) , Fabrice Mériaudeau (2) , Désiré Sidibé (2)

1 VICOROB - Research institute of Computer Vision and Robotics [Girona]
2 Le2i - Laboratoire Electronique, Informatique et Image [UMR6306]

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. Our method is based on Local Binary Patterns (LBP) features to describe the texture of Optical Coherence Tomography (OCT) images and we compare different LBP features extraction approaches to compute a single signature for the whole OCT volume. Experimental results with two datasets of respectively 32 and 30 OCT volumes show that regardless of using low or high level representations, features derived from LBP texture have highly discriminative power. Moreover, the experiments show that the proposed method achieves better classification performances than other recent published works.

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

Domaines: Sciences de l'ingénieur [physics]; Traitement du signal et de l'image [eess.SP]

Guillaume Lemaître, Mojdeh Rastgoo, Joan Massich, Shrinivasan Sankar, Fabrice Mériaudeau, et al.. Classification of SD-OCT Volumes with LBP: Application to DME Detection. Ophthalmic Medical Image Analysis Workshop (OMIA), Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015, Oct 2015, Munich, Germany. ⟨hal-01235888⟩