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Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection
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 irrever...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983398/ https://www.ncbi.nlm.nih.gov/pubmed/27555965 http://dx.doi.org/10.1155/2016/3298606 |
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author | Lemaître, Guillaume Rastgoo, Mojdeh Massich, Joan Cheung, Carol Y. Wong, Tien Y. Lamoureux, Ecosse Milea, Dan Mériaudeau, Fabrice Sidibé, Désiré |
author_facet | Lemaître, Guillaume Rastgoo, Mojdeh Massich, Joan Cheung, Carol Y. Wong, Tien Y. Lamoureux, Ecosse Milea, Dan Mériaudeau, Fabrice Sidibé, Désiré |
author_sort | Lemaître, Guillaume |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4983398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-49833982016-08-23 Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection Lemaître, Guillaume Rastgoo, Mojdeh Massich, Joan Cheung, Carol Y. Wong, Tien Y. Lamoureux, Ecosse Milea, Dan Mériaudeau, Fabrice Sidibé, Désiré J Ophthalmol Research Article 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. Hindawi Publishing Corporation 2016 2016-07-31 /pmc/articles/PMC4983398/ /pubmed/27555965 http://dx.doi.org/10.1155/2016/3298606 Text en Copyright © 2016 Guillaume Lemaître et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Lemaître, Guillaume Rastgoo, Mojdeh Massich, Joan Cheung, Carol Y. Wong, Tien Y. Lamoureux, Ecosse Milea, Dan Mériaudeau, Fabrice Sidibé, Désiré Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection |
title | Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection |
title_full | Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection |
title_fullStr | Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection |
title_full_unstemmed | Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection |
title_short | Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection |
title_sort | classification of sd-oct volumes using local binary patterns: experimental validation for dme detection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983398/ https://www.ncbi.nlm.nih.gov/pubmed/27555965 http://dx.doi.org/10.1155/2016/3298606 |
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