Adaptive and quality 3D meshing from imaging data

  • Authors:

  • Yongjie Zhang;Chandrajit Bajaj;Bong-Soo Sohn

  • Affiliations:

  • The University of Texas at Austin, Austin, TX;The University of Texas at Austin, Austin, TX;The University of Texas at Austin, Austin, TX

  • Venue:
  • SM '03 Proceedings of the eighth ACM symposium on Solid modeling and applications
  • Year:
  • 2003

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Abstract

This paper presents an algorithm to extract adaptive and quality 3D meshes directly from volumetric imaging data - primarily Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The extracted tetrahedral and hexahedral meshes are extensively used in finite element simulations. Our comprehensive approach combines bilateral and anisotropic (feature specific) diffusion filtering, with contour spectrum based, isosurface and interval volume selection. Next, a top-down octree subdivision coupled with the dual contouring method is used to rapidly extract adaptive 3D finite element meshes from volumetric imaging data. The main contributions are extending the dual contouring method to crack free interval volume tetrahedralization and hexahedralization with feature sensitive adaptation. Compared to other tetrahedral extraction methods from imaging data, our method generates better quality adaptive 3D meshes without hanging nodes. Our method has the properties of crack prevention and feature sensitivity.