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

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 t...

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Main Authors: Sidibé, D., Sankar, S., Lemaître, G., Rastgoo, M., Massich, J., Cheung, C.Y., Tan, G.S.W., Milea, D., Lamoureux, E., Wong, T.Y., Mériaudeau, F.
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Published: Elsevier Ireland Ltd 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995655027&doi=10.1016%2fj.cmpb.2016.11.001&partnerID=40&md5=730b22c8872bda4e7130883365c3c564
http://eprints.utp.edu.my/19645/
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spelling my.utp.eprints.196452018-04-20T07:23:57Z An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images Sidibé, D. Sankar, S. Lemaître, G. Rastgoo, M. Massich, J. Cheung, C.Y. Tan, G.S.W. Milea, D. Lamoureux, E. Wong, T.Y. Mériaudeau, F. 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. © 2016 Elsevier Ireland Ltd Elsevier Ireland Ltd 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995655027&doi=10.1016%2fj.cmpb.2016.11.001&partnerID=40&md5=730b22c8872bda4e7130883365c3c564 Sidibé, D. and Sankar, S. and Lemaître, G. and Rastgoo, M. and Massich, J. and Cheung, C.Y. and Tan, G.S.W. and Milea, D. and Lamoureux, E. and Wong, T.Y. and Mériaudeau, F. (2017) An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images. Computer Methods and Programs in Biomedicine, 139 . pp. 109-117. http://eprints.utp.edu.my/19645/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description 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. © 2016 Elsevier Ireland Ltd
format Article
author Sidibé, D.
Sankar, S.
Lemaître, G.
Rastgoo, M.
Massich, J.
Cheung, C.Y.
Tan, G.S.W.
Milea, D.
Lamoureux, E.
Wong, T.Y.
Mériaudeau, F.
spellingShingle Sidibé, D.
Sankar, S.
Lemaître, G.
Rastgoo, M.
Massich, J.
Cheung, C.Y.
Tan, G.S.W.
Milea, D.
Lamoureux, E.
Wong, T.Y.
Mériaudeau, F.
An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images
author_facet Sidibé, D.
Sankar, S.
Lemaître, G.
Rastgoo, M.
Massich, J.
Cheung, C.Y.
Tan, G.S.W.
Milea, D.
Lamoureux, E.
Wong, T.Y.
Mériaudeau, F.
author_sort Sidibé, D.
title An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images
title_short An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images
title_full An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images
title_fullStr An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images
title_full_unstemmed An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images
title_sort anomaly detection approach for the identification of dme patients using spectral domain optical coherence tomography images
publisher Elsevier Ireland Ltd
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995655027&doi=10.1016%2fj.cmpb.2016.11.001&partnerID=40&md5=730b22c8872bda4e7130883365c3c564
http://eprints.utp.edu.my/19645/
_version_ 1738656099494526976
score 13.211869

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