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Generating smooth surface meshes from multi-region medical images.

Authors :
d'Otreppe, Vinciane
Boman, Romain
Ponthot, Jean-Philippe
Source :
International Journal for Numerical Methods in Biomedical Engineering; Jun/Jul2012, Vol. 28 Issue 6/7, p642-660, 19p
Publication Year :
2012

Abstract

SUMMARY Thanks to advances in medical imaging technologies and numerical methods, patient-specific modelling is more and more used to improve diagnosis and to estimate the outcome of surgical interventions. It requires the extraction of the domain of interest from the medical scans of the patient, as well as the discretisation of this geometry. However, extracting smooth multi-material meshes that conform to the tissue boundaries described in the segmented image is still an active field of research. We propose to solve this issue by combining an implicit surface reconstruction method with a multi-region mesh extraction scheme. The surface reconstruction algorithm is based on multi-level partition of unity implicit surfaces, which we extended to the multi-material case. The mesh generation algorithm consists in a novel multi-domain version of the marching tetrahedra. It generates multi-region meshes as a set of triangular surface patches consistently joining each other at material junctions. This paper presents this original meshing strategy, starting from boundary points extraction from the segmented data to heterogeneous implicit surface definition, multi-region surface triangulation and mesh adaptation. Results indicate that the proposed approach produces smooth and high-quality triangular meshes with a reasonable geometric accuracy. Hence, the proposed method is well suited for subsequent volume mesh generation and finite element simulations. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20407939
Volume :
28
Issue :
6/7
Database :
Complementary Index
Journal :
International Journal for Numerical Methods in Biomedical Engineering
Publication Type :
Academic Journal
Accession number :
76575075
Full Text :
https://doi.org/10.1002/cnm.1471