An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images.

Published

Journal Article

This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual diseased B-scans inside the volume. Our approach is based on modeling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers. The classification of an OCT volume is based on the number of detected outliers. Experimental results with two different datasets show that the proposed method achieves a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, the experiments show that the proposed method achieves better classification performance than other recently published works.

Full Text

Duke Authors

Cited Authors

  • Sidibé, D; Sankar, S; Lemaître, G; Rastgoo, M; Massich, J; Cheung, CY; Tan, GSW; Milea, D; Lamoureux, E; Wong, TY; Mériaudeau, F

Published Date

  • February 2017

Published In

Volume / Issue

  • 139 /

Start / End Page

  • 109 - 117

PubMed ID

  • 28187882

Pubmed Central ID

  • 28187882

Electronic International Standard Serial Number (EISSN)

  • 1872-7565

International Standard Serial Number (ISSN)

  • 0169-2607

Digital Object Identifier (DOI)

  • 10.1016/j.cmpb.2016.11.001

Language

  • eng