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A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information

We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then c...

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Detalles Bibliográficos
Autores principales: Jiang, Huiyan, He, Baochun, Fang, Di, Ma, Zhiyuan, Yang, Benqiang, Zhang, Libo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3845438/
https://www.ncbi.nlm.nih.gov/pubmed/24324524
http://dx.doi.org/10.1155/2013/743870
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author Jiang, Huiyan
He, Baochun
Fang, Di
Ma, Zhiyuan
Yang, Benqiang
Zhang, Libo
author_facet Jiang, Huiyan
He, Baochun
Fang, Di
Ma, Zhiyuan
Yang, Benqiang
Zhang, Libo
author_sort Jiang, Huiyan
collection PubMed
description We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then combined edge information with primary feature direction computes the vascular structure's center points as the seed points of region growing segmentation. At last, the improved region growing method with branch-based growth strategy is used to segment the vessels. To prove the effectiveness of our algorithm, we use the retinal and abdomen liver vascular CT images to do experiments. The results show that the proposed vessel segmentation algorithm can not only extract the high quality target vessel region, but also can effectively reduce the manual intervention.
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spelling pubmed-38454382013-12-09 A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information Jiang, Huiyan He, Baochun Fang, Di Ma, Zhiyuan Yang, Benqiang Zhang, Libo Comput Math Methods Med Research Article We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then combined edge information with primary feature direction computes the vascular structure's center points as the seed points of region growing segmentation. At last, the improved region growing method with branch-based growth strategy is used to segment the vessels. To prove the effectiveness of our algorithm, we use the retinal and abdomen liver vascular CT images to do experiments. The results show that the proposed vessel segmentation algorithm can not only extract the high quality target vessel region, but also can effectively reduce the manual intervention. Hindawi Publishing Corporation 2013 2013-11-13 /pmc/articles/PMC3845438/ /pubmed/24324524 http://dx.doi.org/10.1155/2013/743870 Text en Copyright © 2013 Huiyan Jiang et al. https://creativecommons.org/licenses/by/3.0/ 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
Jiang, Huiyan
He, Baochun
Fang, Di
Ma, Zhiyuan
Yang, Benqiang
Zhang, Libo
A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information
title A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information
title_full A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information
title_fullStr A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information
title_full_unstemmed A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information
title_short A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information
title_sort region growing vessel segmentation algorithm based on spectrum information
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3845438/
https://www.ncbi.nlm.nih.gov/pubmed/24324524
http://dx.doi.org/10.1155/2013/743870
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