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Energy Minimization in Medical Image Analysis: Methodologies & Applications

Feng Zhao, Xianghua Xie Orcid Logo

International Journal for Numerical Methods in Biomedical Engineering, Volume: 32, Issue: 2

Swansea University Author: Xianghua Xie Orcid Logo

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DOI (Published version): 10.1002/cnm.2733

Abstract

Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two c...

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Published in: International Journal for Numerical Methods in Biomedical Engineering
Published: 2015
URI: https://cronfa.swan.ac.uk/Record/cronfa22240
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first_indexed 2015-07-02T02:07:57Z
last_indexed 2018-02-09T05:00:31Z
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2016-06-13T14:28:16.8156479</datestamp><bib-version>v2</bib-version><id>22240</id><entry>2015-07-01</entry><title>Energy Minimization in Medical Image Analysis: Methodologies &amp; Applications</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>2015-07-01</date><deptcode>SCS</deptcode><abstract>Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree- reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, e.g., image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well.</abstract><type>Journal Article</type><journal>International Journal for Numerical Methods in Biomedical Engineering</journal><volume>32</volume><journalNumber>2</journalNumber><publisher/><keywords>Energy minimisation, medial image analysis, computer vision</keywords><publishedDay>31</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2015</publishedYear><publishedDate>2015-08-31</publishedDate><doi>10.1002/cnm.2733</doi><url/><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2016-06-13T14:28:16.8156479</lastEdited><Created>2015-07-01T10:54:17.3129315</Created><path><level id="1">College of Science</level><level id="2">Computer Science</level></path><authors><author><firstname>Feng</firstname><surname>Zhao</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 2016-06-13T14:28:16.8156479 v2 22240 2015-07-01 Energy Minimization in Medical Image Analysis: Methodologies & Applications b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2015-07-01 SCS Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree- reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, e.g., image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well. Journal Article International Journal for Numerical Methods in Biomedical Engineering 32 2 Energy minimisation, medial image analysis, computer vision 31 8 2015 2015-08-31 10.1002/cnm.2733 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2016-06-13T14:28:16.8156479 2015-07-01T10:54:17.3129315 College of Science Computer Science Feng Zhao 1 Xianghua Xie 0000-0002-2701-8660 2
title Energy Minimization in Medical Image Analysis: Methodologies & Applications
spellingShingle Energy Minimization in Medical Image Analysis: Methodologies & Applications
Xianghua Xie
title_short Energy Minimization in Medical Image Analysis: Methodologies & Applications
title_full Energy Minimization in Medical Image Analysis: Methodologies & Applications
title_fullStr Energy Minimization in Medical Image Analysis: Methodologies & Applications
title_full_unstemmed Energy Minimization in Medical Image Analysis: Methodologies & Applications
title_sort Energy Minimization in Medical Image Analysis: Methodologies & Applications
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Xianghua Xie
author2 Feng Zhao
Xianghua Xie
format Journal article
container_title International Journal for Numerical Methods in Biomedical Engineering
container_volume 32
container_issue 2
publishDate 2015
institution Swansea University
doi_str_mv 10.1002/cnm.2733
college_str College of Science
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hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
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description Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree- reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, e.g., image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well.
published_date 2015-08-31T03:32:48Z
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score 10.896665