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Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation
Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Do...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Optical Society of America
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3408910/ https://www.ncbi.nlm.nih.gov/pubmed/20940837 http://dx.doi.org/10.1364/OE.18.019413 |
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author | Chiu, Stephanie J. Li, Xiao T. Nicholas, Peter Toth, Cynthia A. Izatt, Joseph A. Farsiu, Sina |
author_facet | Chiu, Stephanie J. Li, Xiao T. Nicholas, Peter Toth, Cynthia A. Izatt, Joseph A. Farsiu, Sina |
author_sort | Chiu, Stephanie J. |
collection | PubMed |
description | Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader. |
format | Online Article Text |
id | pubmed-3408910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Optical Society of America |
record_format | MEDLINE/PubMed |
spelling | pubmed-34089102012-10-01 Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation Chiu, Stephanie J. Li, Xiao T. Nicholas, Peter Toth, Cynthia A. Izatt, Joseph A. Farsiu, Sina Opt Express Research-Article Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader. Optical Society of America 2010-08-27 /pmc/articles/PMC3408910/ /pubmed/20940837 http://dx.doi.org/10.1364/OE.18.019413 Text en ©2010 Optical Society of America http://creativecommons.org/licenses/by-nc-nd/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 Unported License, which permits download and redistribution, provided that the original work is properly cited. This license restricts the article from being modified or used commercially. |
spellingShingle | Research-Article Chiu, Stephanie J. Li, Xiao T. Nicholas, Peter Toth, Cynthia A. Izatt, Joseph A. Farsiu, Sina Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation |
title | Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation |
title_full | Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation |
title_fullStr | Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation |
title_full_unstemmed | Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation |
title_short | Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation |
title_sort | automatic segmentation of seven retinal layers in sdoct images congruent with expert manual segmentation |
topic | Research-Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3408910/ https://www.ncbi.nlm.nih.gov/pubmed/20940837 http://dx.doi.org/10.1364/OE.18.019413 |
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