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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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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 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.
Keywords: Medical image analysis, mesh processing
College: Faculty of Science and Engineering
Start Page: 1
End Page: 6

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