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

Overview

Journal J Ophthalmol

Publisher Wiley

Specialty Ophthalmology

Date 2016 Aug 25

PMID 27555965

Citations 21

Authors

Guillaume Lemaitre

Mojdeh Rastgoo

Joan Massich

Carol Y Cheung

Tien Y Wong

Ecosse Lamoureux

Dan Milea

Fabrice Meriaudeau

Desire Sidibe

Affiliations

Soon will be listed here.

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. Our method 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.

Citing Articles

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Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images.

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