Peer Reviewed

1

DOI

10.17077/omia.1021

Conference Location

Munich, Germany

Publication Date

October 2015

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. 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.

Rights

Copyright © 2015 Guillaume Lemaître, Mojdeh Rastgoo, Joan Massich, Shrinivasan Sankar, Fabrice Mériaudeau, and Désiré Sidibé

Included in

Ophthalmology Commons

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Oct 9th, 12:00 AM Oct 9th, 12:00 AM

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

Munich, Germany

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.