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Segmentation and Tracking of Coronary Arterial Wall

Ehab Essa, Xianghua Xie Orcid Logo

Computational and Mathematical Biomedical Engineering

Swansea University Author: Xianghua Xie Orcid Logo

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Abstract

In this paper, we present a segmentation and tracking method based on hidden Markov model (HMM) to detect the outer coronary arterial wall in intravascular images. The proposed method tracks a set of hidden states representing the border location on a set of normal lines obtained from the previ- ous...

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Published in: Computational and Mathematical Biomedical Engineering
ISSN: 2227-3085 2227-9385
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa32107
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first_indexed 2017-02-25T05:04:38Z
last_indexed 2018-02-09T05:19:41Z
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2017-05-19T19:14:29.4563598</datestamp><bib-version>v2</bib-version><id>32107</id><entry>2017-02-24</entry><title>Segmentation and Tracking of Coronary Arterial Wall</title><swanseaauthors><author><sid>b334d40963c7a2f435f06d2c26c74e11</sid><ORCID>0000-0002-2701-8660</ORCID><firstname>Xianghua</firstname><surname>Xie</surname><name>Xianghua Xie</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2017-02-24</date><deptcode>SCS</deptcode><abstract>In this paper, we present a segmentation and tracking method based on hidden Markov model (HMM) to detect the outer coronary arterial wall in intravascular images. The proposed method tracks a set of hidden states representing the border location on a set of normal lines obtained from the previ- ous frame. The border observation is derived from a classification-based cost function and a shape prior model. The emission probability is defined based on two Gaussian probability distributions for the vessel border and background. The transition probability is learned by using the Baum-Welch algorithm. The optimal sequence of the hidden states is obtained by using Viterbi algorithm. The proposed method shows promising results on tracking and segmenting the arterial wall.</abstract><type>Conference Paper/Proceeding/Abstract</type><journal>Computational and Mathematical Biomedical Engineering</journal><publisher/><issnPrint>2227-3085</issnPrint><issnElectronic>2227-9385</issnElectronic><keywords>Image analysis, machine learning</keywords><publishedDay>30</publishedDay><publishedMonth>4</publishedMonth><publishedYear>2017</publishedYear><publishedDate>2017-04-30</publishedDate><doi/><url>http://www.compbiomed.net/getfile.php?type=13/site_documents&amp;amp;id=CMBE17Vol1prepress_2227-9385.pdf</url><notes>The paper is available freely online as part of CMBE 2017 Proceedings Vol. 1: http://www.compbiomed.net/2017/cmbe-proceedings.htm</notes><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2017-05-19T19:14:29.4563598</lastEdited><Created>2017-02-24T23:42:23.2106693</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Ehab</firstname><surname>Essa</surname><order>1</order></author><author><firstname>Xianghua</firstname><surname>Xie</surname><orcid>0000-0002-2701-8660</orcid><order>2</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2017-05-19T19:14:29.4563598 v2 32107 2017-02-24 Segmentation and Tracking of Coronary Arterial Wall b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2017-02-24 SCS In this paper, we present a segmentation and tracking method based on hidden Markov model (HMM) to detect the outer coronary arterial wall in intravascular images. The proposed method tracks a set of hidden states representing the border location on a set of normal lines obtained from the previ- ous frame. The border observation is derived from a classification-based cost function and a shape prior model. The emission probability is defined based on two Gaussian probability distributions for the vessel border and background. The transition probability is learned by using the Baum-Welch algorithm. The optimal sequence of the hidden states is obtained by using Viterbi algorithm. The proposed method shows promising results on tracking and segmenting the arterial wall. Conference Paper/Proceeding/Abstract Computational and Mathematical Biomedical Engineering 2227-3085 2227-9385 Image analysis, machine learning 30 4 2017 2017-04-30 http://www.compbiomed.net/getfile.php?type=13/site_documents&amp;id=CMBE17Vol1prepress_2227-9385.pdf The paper is available freely online as part of CMBE 2017 Proceedings Vol. 1: http://www.compbiomed.net/2017/cmbe-proceedings.htm COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2017-05-19T19:14:29.4563598 2017-02-24T23:42:23.2106693 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Ehab Essa 1 Xianghua Xie 0000-0002-2701-8660 2
title Segmentation and Tracking of Coronary Arterial Wall
spellingShingle Segmentation and Tracking of Coronary Arterial Wall
Xianghua Xie
title_short Segmentation and Tracking of Coronary Arterial Wall
title_full Segmentation and Tracking of Coronary Arterial Wall
title_fullStr Segmentation and Tracking of Coronary Arterial Wall
title_full_unstemmed Segmentation and Tracking of Coronary Arterial Wall
title_sort Segmentation and Tracking of Coronary Arterial Wall
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Xianghua Xie
author2 Ehab Essa
Xianghua Xie
format Conference Paper/Proceeding/Abstract
container_title Computational and Mathematical Biomedical Engineering
publishDate 2017
institution Swansea University
issn 2227-3085
2227-9385
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 Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://www.compbiomed.net/getfile.php?type=13/site_documents&amp;id=CMBE17Vol1prepress_2227-9385.pdf
document_store_str 0
active_str 0
description In this paper, we present a segmentation and tracking method based on hidden Markov model (HMM) to detect the outer coronary arterial wall in intravascular images. The proposed method tracks a set of hidden states representing the border location on a set of normal lines obtained from the previ- ous frame. The border observation is derived from a classification-based cost function and a shape prior model. The emission probability is defined based on two Gaussian probability distributions for the vessel border and background. The transition probability is learned by using the Baum-Welch algorithm. The optimal sequence of the hidden states is obtained by using Viterbi algorithm. The proposed method shows promising results on tracking and segmenting the arterial wall.
published_date 2017-04-30T03:39:17Z
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score 11.016994

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