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Robust generation of high‐quality unstructured meshes on realistic biomedical geometry
Author(s) -
Ito Yasushi,
Corey Shum P.,
Shih Alan M.,
Soni Bharat K.,
Nakahashi Kazuhiro
Publication year - 2005
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.1482
Subject(s) - volume mesh , polygon mesh , mesh generation , computer science , computational science , complex geometry , visualization , surface (topology) , computer graphics (images) , tetrahedron , geometry , algorithm , computer vision , artificial intelligence , mathematics , finite element method , engineering , structural engineering
In this paper, we propose efficient and robust unstructured mesh generation methods based on computed tomography (CT) and magnetic resonance imaging (MRI) data, in order to obtain a patient‐specific geometry for high‐fidelity numerical simulations. Surface extraction from medical images is carried out mainly using open source libraries, including the Insight Segmentation and Registration Toolkit and the Visualization Toolkit, into the form of facet surface representation. To create high‐quality surface meshes, we propose two approaches. One is a direct advancing front method, and the other is a modified decimation method. The former emphasizes the controllability of local mesh density, and the latter enables semi‐automated mesh generation from low‐quality discrete surfaces. An advancing‐front‐based volume meshing method is employed. Our approaches are demonstrated with high‐fidelity tetrahedral meshes around medical geometries extracted from CT/MRI data. Copyright © 2005 John Wiley & Sons, Ltd.