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Segmentation of biomedical images using active contour model with robust image feature and shape prior

Si Yong Yeo, Xianghua Xie Orcid Logo, Igor Sazonov Orcid Logo, Perumal Nithiarasu Orcid Logo

International Journal for Numerical Methods in Biomedical Engineering, Volume: 30, Issue: 2, Pages: 232 - 248

Swansea University Authors: Xianghua Xie Orcid Logo, Igor Sazonov Orcid Logo, Perumal Nithiarasu Orcid Logo

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DOI (Published version): 10.1002/cnm.2600

Abstract

In this article, a new level set model is proposed for the segmentation of biomedical images. The image energy of the proposed model is derived from a robust image gradient feature which gives the active contour a global representation of the geometric configuration, making it more robust in dealing...

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Published in: International Journal for Numerical Methods in Biomedical Engineering
ISSN: 2040-7939
Published: 2014
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URI: https://cronfa.swan.ac.uk/Record/cronfa20249
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first_indexed 2015-03-03T02:59:53Z
last_indexed 2020-09-22T02:34:02Z
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spelling 2020-09-21T11:36:24.3334917 v2 20249 2015-03-02 Segmentation of biomedical images using active contour model with robust image feature and shape prior b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 05a507952e26462561085fb6f62c8897 0000-0001-6685-2351 Igor Sazonov Igor Sazonov true false 3b28bf59358fc2b9bd9a46897dbfc92d 0000-0002-4901-2980 Perumal Nithiarasu Perumal Nithiarasu true false 2015-03-02 SCS In this article, a new level set model is proposed for the segmentation of biomedical images. The image energy of the proposed model is derived from a robust image gradient feature which gives the active contour a global representation of the geometric configuration, making it more robust in dealing with image noise, weak edges, and initial configurations. Statistical shape information is incorporated using nonparametric shape density distribution, which allows the shape model to handle relatively large shape variations. The segmentation of various shapes from both synthetic and real images depict the robustness and efficiency of the proposed method. Journal Article International Journal for Numerical Methods in Biomedical Engineering 30 2 232 248 2040-7939 3 2 2014 2014-02-03 10.1002/cnm.2600 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2020-09-21T11:36:24.3334917 2015-03-02T16:16:05.0489051 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Si Yong Yeo 1 Xianghua Xie 0000-0002-2701-8660 2 Igor Sazonov 0000-0001-6685-2351 3 Perumal Nithiarasu 0000-0002-4901-2980 4 0020249-21062015153903.pdf yeo14.pdf 2015-06-21T16:21:35.3930000 Output 5475638 application/pdf Version of Record true 2015-06-21T00:00:00.0000000 true
title Segmentation of biomedical images using active contour model with robust image feature and shape prior
spellingShingle Segmentation of biomedical images using active contour model with robust image feature and shape prior
Xianghua Xie
Igor Sazonov
Perumal Nithiarasu
title_short Segmentation of biomedical images using active contour model with robust image feature and shape prior
title_full Segmentation of biomedical images using active contour model with robust image feature and shape prior
title_fullStr Segmentation of biomedical images using active contour model with robust image feature and shape prior
title_full_unstemmed Segmentation of biomedical images using active contour model with robust image feature and shape prior
title_sort Segmentation of biomedical images using active contour model with robust image feature and shape prior
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
05a507952e26462561085fb6f62c8897
3b28bf59358fc2b9bd9a46897dbfc92d
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
05a507952e26462561085fb6f62c8897_***_Igor Sazonov
3b28bf59358fc2b9bd9a46897dbfc92d_***_Perumal Nithiarasu
author Xianghua Xie
Igor Sazonov
Perumal Nithiarasu
author2 Si Yong Yeo
Xianghua Xie
Igor Sazonov
Perumal Nithiarasu
format Journal article
container_title International Journal for Numerical Methods in Biomedical Engineering
container_volume 30
container_issue 2
container_start_page 232
publishDate 2014
institution Swansea University
issn 2040-7939
doi_str_mv 10.1002/cnm.2600
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description In this article, a new level set model is proposed for the segmentation of biomedical images. The image energy of the proposed model is derived from a robust image gradient feature which gives the active contour a global representation of the geometric configuration, making it more robust in dealing with image noise, weak edges, and initial configurations. Statistical shape information is incorporated using nonparametric shape density distribution, which allows the shape model to handle relatively large shape variations. The segmentation of various shapes from both synthetic and real images depict the robustness and efficiency of the proposed method.
published_date 2014-02-03T03:23:52Z
_version_ 1763750793497804800
score 10.999207

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