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Classifying DME vs normal SD-OCT volumes: A review

This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a...

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Main Authors: Massich, J., Rastgoo, M., Lemaître, G., Cheung, C.Y., Wong, T.Y., Sidibé, D., Mériaudeau, F.
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019112491&doi=10.1109%2fICPR.2016.7899816&partnerID=40&md5=5f595f427e72ba56e60014febb1f6116
http://eprints.utp.edu.my/20097/
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spelling my.utp.eprints.200972018-04-22T14:41:05Z Classifying DME vs normal SD-OCT volumes: A review Massich, J. Rastgoo, M. Lemaître, G. Cheung, C.Y. Wong, T.Y. Sidibé, D. Mériaudeau, F. This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this common benchmark and dataset to produce reliable comparison. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019112491&doi=10.1109%2fICPR.2016.7899816&partnerID=40&md5=5f595f427e72ba56e60014febb1f6116 Massich, J. and Rastgoo, M. and Lemaître, G. and Cheung, C.Y. and Wong, T.Y. and Sidibé, D. and Mériaudeau, F. (2017) Classifying DME vs normal SD-OCT volumes: A review. Proceedings - International Conference on Pattern Recognition . pp. 1297-1302. http://eprints.utp.edu.my/20097/
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 article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this common benchmark and dataset to produce reliable comparison. © 2016 IEEE.
format Article
author Massich, J.
Rastgoo, M.
Lemaître, G.
Cheung, C.Y.
Wong, T.Y.
Sidibé, D.
Mériaudeau, F.
spellingShingle Massich, J.
Rastgoo, M.
Lemaître, G.
Cheung, C.Y.
Wong, T.Y.
Sidibé, D.
Mériaudeau, F.
Classifying DME vs normal SD-OCT volumes: A review
author_facet Massich, J.
Rastgoo, M.
Lemaître, G.
Cheung, C.Y.
Wong, T.Y.
Sidibé, D.
Mériaudeau, F.
author_sort Massich, J.
title Classifying DME vs normal SD-OCT volumes: A review
title_short Classifying DME vs normal SD-OCT volumes: A review
title_full Classifying DME vs normal SD-OCT volumes: A review
title_fullStr Classifying DME vs normal SD-OCT volumes: A review
title_full_unstemmed Classifying DME vs normal SD-OCT volumes: A review
title_sort classifying dme vs normal sd-oct volumes: a review
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019112491&doi=10.1109%2fICPR.2016.7899816&partnerID=40&md5=5f595f427e72ba56e60014febb1f6116
http://eprints.utp.edu.my/20097/
_version_ 1738656163180838912
score 13.211869

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