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Conference Paper/Proceeding/Abstract 1303 views

Computing 3D Mesh Correspondence for Aortic Root Shape Modelling

Robert Palmer, Gary Tam Orcid Logo, Rob Alock, Carl Roobottom, Xianghua Xie Orcid Logo

19th Conference on Medical Image Understanding and Analysis, Pages: 1 - 6

Swansea University Authors: Gary Tam Orcid Logo, Xianghua Xie Orcid Logo

Abstract

Aortic valve disorder is one of the common diseases affecting elderly people. To provide visual assessment and improve success of surgical treatment, a segmentation technique equipped with a reliable statistical shape model is required. This in turn requires reliable dense correspondences establishm...

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Published in: 19th Conference on Medical Image Understanding and Analysis
Published: 19th Conference on Medical Image Understanding and Analysis 2015
Online Access: http://csvision.swan.ac.uk/uploads/Site/PublicationCategorisedVersion/miua2015_XX.pdf
URI: https://cronfa.swan.ac.uk/Record/cronfa22234
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first_indexed 2015-07-02T02:07:56Z
last_indexed 2019-10-14T13:30:13Z
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spelling 2019-10-14T12:17:37.2071283 v2 22234 2015-07-01 Computing 3D Mesh Correspondence for Aortic Root Shape Modelling e75a68e11a20e5f1da94ee6e28ff5e76 0000-0001-7387-5180 Gary Tam Gary Tam true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2015-07-01 SCS Aortic valve disorder is one of the common diseases affecting elderly people. To provide visual assessment and improve success of surgical treatment, a segmentation technique equipped with a reliable statistical shape model is required. This in turn requires reliable dense correspondences establishment. This paper develops a reliable 3D registration technique targeting aortic region. Given a few easily identifiable land- mark correspondences, our technique obtains a much denser set of point correspondences across a set of 3D aortic sources meshes to the target mesh. We proposes to use geodesic interpolation, a new mesh based similarity metric, and a two-stage local transformation to develop a better registration technique for 3D aortic meshes. It results in better corre- spondences compared to existing work, shows an average Hausdorff distance of 3.65mm and point-to-mesh distance of 0.41mm. Visual comparison is also provided to assess the quality of the point correspondences. Conference Paper/Proceeding/Abstract 19th Conference on Medical Image Understanding and Analysis 1 6 19th Conference on Medical Image Understanding and Analysis Medical image analysis, mesh processing 31 7 2015 2015-07-31 http://csvision.swan.ac.uk/uploads/Site/PublicationCategorisedVersion/miua2015_XX.pdf COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2019-10-14T12:17:37.2071283 2015-07-01T10:13:14.4937573 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Robert Palmer 1 Gary Tam 0000-0001-7387-5180 2 Rob Alock 3 Carl Roobottom 4 Xianghua Xie 0000-0002-2701-8660 5
title Computing 3D Mesh Correspondence for Aortic Root Shape Modelling
spellingShingle Computing 3D Mesh Correspondence for Aortic Root Shape Modelling
Gary Tam
Xianghua Xie
title_short Computing 3D Mesh Correspondence for Aortic Root Shape Modelling
title_full Computing 3D Mesh Correspondence for Aortic Root Shape Modelling
title_fullStr Computing 3D Mesh Correspondence for Aortic Root Shape Modelling
title_full_unstemmed Computing 3D Mesh Correspondence for Aortic Root Shape Modelling
title_sort Computing 3D Mesh Correspondence for Aortic Root Shape Modelling
author_id_str_mv e75a68e11a20e5f1da94ee6e28ff5e76
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv e75a68e11a20e5f1da94ee6e28ff5e76_***_Gary Tam
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Gary Tam
Xianghua Xie
author2 Robert Palmer
Gary Tam
Rob Alock
Carl Roobottom
Xianghua Xie
format Conference Paper/Proceeding/Abstract
container_title 19th Conference on Medical Image Understanding and Analysis
container_start_page 1
publishDate 2015
institution Swansea University
publisher 19th Conference on Medical Image Understanding and Analysis
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://csvision.swan.ac.uk/uploads/Site/PublicationCategorisedVersion/miua2015_XX.pdf
document_store_str 0
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description Aortic valve disorder is one of the common diseases affecting elderly people. To provide visual assessment and improve success of surgical treatment, a segmentation technique equipped with a reliable statistical shape model is required. This in turn requires reliable dense correspondences establishment. This paper develops a reliable 3D registration technique targeting aortic region. Given a few easily identifiable land- mark correspondences, our technique obtains a much denser set of point correspondences across a set of 3D aortic sources meshes to the target mesh. We proposes to use geodesic interpolation, a new mesh based similarity metric, and a two-stage local transformation to develop a better registration technique for 3D aortic meshes. It results in better corre- spondences compared to existing work, shows an average Hausdorff distance of 3.65mm and point-to-mesh distance of 0.41mm. Visual comparison is also provided to assess the quality of the point correspondences.
published_date 2015-07-31T03:26:28Z
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score 11.016235

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