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Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification

Retinal blood vessels have a significant role in the diagnosis and treatment of various retinal diseases such as diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. For this reason, retinal vasculature extraction is important in order to help specialists for the diagnosis and treatme...

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Detalles Bibliográficos
Autores principales: Yavuz, Zafer, Köse, Cemal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559979/
https://www.ncbi.nlm.nih.gov/pubmed/29065611
http://dx.doi.org/10.1155/2017/4897258
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author Yavuz, Zafer
Köse, Cemal
author_facet Yavuz, Zafer
Köse, Cemal
author_sort Yavuz, Zafer
collection PubMed
description Retinal blood vessels have a significant role in the diagnosis and treatment of various retinal diseases such as diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. For this reason, retinal vasculature extraction is important in order to help specialists for the diagnosis and treatment of systematic diseases. In this paper, a novel approach is developed to extract retinal blood vessel network. Our method comprises four stages: (1) preprocessing stage in order to prepare dataset for segmentation; (2) an enhancement procedure including Gabor, Frangi, and Gauss filters obtained separately before a top-hat transform; (3) a hard and soft clustering stage which includes K-means and Fuzzy C-means (FCM) in order to get binary vessel map; and (4) a postprocessing step which removes falsely segmented isolated regions. The method is tested on color retinal images obtained from STARE and DRIVE databases which are available online. As a result, Gabor filter followed by K-means clustering method achieves 95.94% and 95.71% of accuracy for STARE and DRIVE databases, respectively, which are acceptable for diagnosis systems.
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spelling pubmed-55599792017-08-24 Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification Yavuz, Zafer Köse, Cemal J Healthc Eng Research Article Retinal blood vessels have a significant role in the diagnosis and treatment of various retinal diseases such as diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. For this reason, retinal vasculature extraction is important in order to help specialists for the diagnosis and treatment of systematic diseases. In this paper, a novel approach is developed to extract retinal blood vessel network. Our method comprises four stages: (1) preprocessing stage in order to prepare dataset for segmentation; (2) an enhancement procedure including Gabor, Frangi, and Gauss filters obtained separately before a top-hat transform; (3) a hard and soft clustering stage which includes K-means and Fuzzy C-means (FCM) in order to get binary vessel map; and (4) a postprocessing step which removes falsely segmented isolated regions. The method is tested on color retinal images obtained from STARE and DRIVE databases which are available online. As a result, Gabor filter followed by K-means clustering method achieves 95.94% and 95.71% of accuracy for STARE and DRIVE databases, respectively, which are acceptable for diagnosis systems. Hindawi 2017 2017-08-03 /pmc/articles/PMC5559979/ /pubmed/29065611 http://dx.doi.org/10.1155/2017/4897258 Text en Copyright © 2017 Zafer Yavuz and Cemal Köse. http://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
Yavuz, Zafer
Köse, Cemal
Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification
title Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification
title_full Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification
title_fullStr Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification
title_full_unstemmed Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification
title_short Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification
title_sort blood vessel extraction in color retinal fundus images with enhancement filtering and unsupervised classification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559979/
https://www.ncbi.nlm.nih.gov/pubmed/29065611
http://dx.doi.org/10.1155/2017/4897258
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