Cargando…
Feature Learning Based Random Walk for Liver Segmentation
Liver segmentation is a significant processing technique for computer-assisted diagnosis. This method has attracted considerable attention and achieved effective result. However, liver segmentation using computed tomography (CT) images remains a challenging task because of the low contrast between t...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5112808/ https://www.ncbi.nlm.nih.gov/pubmed/27846217 http://dx.doi.org/10.1371/journal.pone.0164098 |
_version_ | 1782468078329659392 |
---|---|
author | Zheng, Yongchang Ai, Danni Zhang, Pan Gao, Yefei Xia, Likun Du, Shunda Sang, Xinting Yang, Jian |
author_facet | Zheng, Yongchang Ai, Danni Zhang, Pan Gao, Yefei Xia, Likun Du, Shunda Sang, Xinting Yang, Jian |
author_sort | Zheng, Yongchang |
collection | PubMed |
description | Liver segmentation is a significant processing technique for computer-assisted diagnosis. This method has attracted considerable attention and achieved effective result. However, liver segmentation using computed tomography (CT) images remains a challenging task because of the low contrast between the liver and adjacent organs. This paper proposes a feature-learning-based random walk method for liver segmentation using CT images. Four texture features were extracted and then classified to determine the classification probability corresponding to the test images. Seed points on the original test image were automatically selected and further used in the random walk (RW) algorithm to achieve comparable results to previous segmentation methods. |
format | Online Article Text |
id | pubmed-5112808 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-51128082016-12-08 Feature Learning Based Random Walk for Liver Segmentation Zheng, Yongchang Ai, Danni Zhang, Pan Gao, Yefei Xia, Likun Du, Shunda Sang, Xinting Yang, Jian PLoS One Research Article Liver segmentation is a significant processing technique for computer-assisted diagnosis. This method has attracted considerable attention and achieved effective result. However, liver segmentation using computed tomography (CT) images remains a challenging task because of the low contrast between the liver and adjacent organs. This paper proposes a feature-learning-based random walk method for liver segmentation using CT images. Four texture features were extracted and then classified to determine the classification probability corresponding to the test images. Seed points on the original test image were automatically selected and further used in the random walk (RW) algorithm to achieve comparable results to previous segmentation methods. Public Library of Science 2016-11-15 /pmc/articles/PMC5112808/ /pubmed/27846217 http://dx.doi.org/10.1371/journal.pone.0164098 Text en © 2016 Zheng et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zheng, Yongchang Ai, Danni Zhang, Pan Gao, Yefei Xia, Likun Du, Shunda Sang, Xinting Yang, Jian Feature Learning Based Random Walk for Liver Segmentation |
title | Feature Learning Based Random Walk for Liver Segmentation |
title_full | Feature Learning Based Random Walk for Liver Segmentation |
title_fullStr | Feature Learning Based Random Walk for Liver Segmentation |
title_full_unstemmed | Feature Learning Based Random Walk for Liver Segmentation |
title_short | Feature Learning Based Random Walk for Liver Segmentation |
title_sort | feature learning based random walk for liver segmentation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5112808/ https://www.ncbi.nlm.nih.gov/pubmed/27846217 http://dx.doi.org/10.1371/journal.pone.0164098 |
work_keys_str_mv | AT zhengyongchang featurelearningbasedrandomwalkforliversegmentation AT aidanni featurelearningbasedrandomwalkforliversegmentation AT zhangpan featurelearningbasedrandomwalkforliversegmentation AT gaoyefei featurelearningbasedrandomwalkforliversegmentation AT xialikun featurelearningbasedrandomwalkforliversegmentation AT dushunda featurelearningbasedrandomwalkforliversegmentation AT sangxinting featurelearningbasedrandomwalkforliversegmentation AT yangjian featurelearningbasedrandomwalkforliversegmentation |