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

Guillaume Lemaître,Désiré Sidibé,Tien Y Wong,Carol Y Cheung,Dan Milea,Fabrice Mériaudeau,Ecosse Lamoureux,Joan Massich,Mojdeh Rastgoo

doi:10.1155/2016/3298606

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.

Highlights

Eye diseases such as Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are the most common causes of irreversible vision loss in individuals with diabetes
Early detection and treatment of DR and DME play a major role in preventing adverse effects such as blindness
Random Forest (RF) has a better performance by achieving better SE (81.2%, 75.0%, and 68.7%), while Support Vector Machine (SVM) achieves the highest SP (93.7%), see Table 8 in the Appendix

Summary

Introduction

Eye diseases such as Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are the most common causes of irreversible vision loss in individuals with diabetes. Just in United States alone, health care and associated costs related to eye diseases are estimated at almost $500 M [1]. The prevalent cases of DR are expected to grow exponentially affecting over 300 M people worldwide by 2025 [2]. Given this scenario, early detection and treatment of DR and DME play a major role in preventing adverse effects such as blindness. DME is characterized as an increase in retinal thickness within 1-disk diameter of the fovea center with or without hard exudates and sometimes associated with cysts [3]. Fundus images which have proven to be very useful in revealing most of the eye pathologies [4, 5] are not as good as OCT images which provide information about cross-sectional retinal morphology [6]

Methods

Results

Conclusion