A Data Clustering and Streamline Reduction Method for 3D MR Flow Vector Field Simplification
A Data Clustering and Streamline Reduction Method for 3D MR Flow Vector Field Simplification
With the increasing capability of MR imaging and Computational Fluid Dynamics (CFD) techniques, a significant amount of data related to the haemodynamics of the cardiovascular systems are being generated. Direct visualization of the data introduces unnecessary visual clutter and hides away the underlying trend associated with the progression of the disease. To elucidate the main topological structure of the flow fields, we present in this paper a 3D visualisation method based on the abstraction of complex flow fields. It uses hierarchical clustering and local linear expansion to extract salient topological flow features. This is then combined with 3D streamline tracking, allowing most important flow details to be visualized. Example results of the technique
visualization, 3D flow MRI, clustering, streamlines
451-458
Carmo, Bernardo Silva
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Ng, Y H Pauline
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Prügel-Bennett, Adam
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Yang, Guang-Zhong
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Barillot, C
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Haynor, D R
8271c6f9-79d2-4be4-9202-65d9f34b2dd6
Hellier, P
d834cf58-5d7a-40d0-9730-44e0b398b3ac
2004
Carmo, Bernardo Silva
ff18c8e6-db97-4b6c-b554-f4963b0fc431
Ng, Y H Pauline
27d5c5ab-721e-479a-a27b-cf6d30b61d1b
Prügel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Yang, Guang-Zhong
2bc5f3d5-33d5-498f-9aa0-c5470cf0390d
Barillot, C
458f552d-9666-4155-856c-6042a1ebda84
Haynor, D R
8271c6f9-79d2-4be4-9202-65d9f34b2dd6
Hellier, P
d834cf58-5d7a-40d0-9730-44e0b398b3ac
Carmo, Bernardo Silva, Ng, Y H Pauline, Prügel-Bennett, Adam and Yang, Guang-Zhong
,
Barillot, C, Haynor, D R and Hellier, P
(eds.)
(2004)
A Data Clustering and Streamline Reduction Method for 3D MR Flow Vector Field Simplification.
Lecture Notes in Computer Science, 3216, .
Abstract
With the increasing capability of MR imaging and Computational Fluid Dynamics (CFD) techniques, a significant amount of data related to the haemodynamics of the cardiovascular systems are being generated. Direct visualization of the data introduces unnecessary visual clutter and hides away the underlying trend associated with the progression of the disease. To elucidate the main topological structure of the flow fields, we present in this paper a 3D visualisation method based on the abstraction of complex flow fields. It uses hierarchical clustering and local linear expansion to extract salient topological flow features. This is then combined with 3D streamline tracking, allowing most important flow details to be visualized. Example results of the technique
Text
carmo04miccai.pdf
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More information
Published date: 2004
Keywords:
visualization, 3D flow MRI, clustering, streamlines
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 260905
URI: http://eprints.soton.ac.uk/id/eprint/260905
ISSN: 0302-9743
PURE UUID: 7daa25d6-e62e-4416-b6e1-dffcde6b7738
Catalogue record
Date deposited: 23 May 2005
Last modified: 14 Mar 2024 06:44
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Contributors
Author:
Bernardo Silva Carmo
Author:
Y H Pauline Ng
Author:
Adam Prügel-Bennett
Author:
Guang-Zhong Yang
Editor:
C Barillot
Editor:
D R Haynor
Editor:
P Hellier
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