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Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our impr...

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Detalles Bibliográficos
Autores principales: Jiang, Huiyan, He, Baochun, Ma, Zhiyuan, Zong, Mao, Zhou, Xiangrong, Fujita, Hiroshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3770042/
https://www.ncbi.nlm.nih.gov/pubmed/24066017
http://dx.doi.org/10.1155/2013/958398
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author Jiang, Huiyan
He, Baochun
Ma, Zhiyuan
Zong, Mao
Zhou, Xiangrong
Fujita, Hiroshi
author_facet Jiang, Huiyan
He, Baochun
Ma, Zhiyuan
Zong, Mao
Zhou, Xiangrong
Fujita, Hiroshi
author_sort Jiang, Huiyan
collection PubMed
description A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method.
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spelling pubmed-37700422013-09-24 Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image Jiang, Huiyan He, Baochun Ma, Zhiyuan Zong, Mao Zhou, Xiangrong Fujita, Hiroshi Comput Math Methods Med Research Article A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method. Hindawi Publishing Corporation 2013 2013-08-26 /pmc/articles/PMC3770042/ /pubmed/24066017 http://dx.doi.org/10.1155/2013/958398 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
Ma, Zhiyuan
Zong, Mao
Zhou, Xiangrong
Fujita, Hiroshi
Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image
title Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image
title_full Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image
title_fullStr Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image
title_full_unstemmed Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image
title_short Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image
title_sort liver segmentation based on snakes model and improved growcut algorithm in abdominal ct image
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3770042/
https://www.ncbi.nlm.nih.gov/pubmed/24066017
http://dx.doi.org/10.1155/2013/958398
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