Adaptive and quality 3D meshing from imaging data

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.

[1]  Jack Snoeyink,et al.  Computing contour trees in all dimensions , 2000, SODA '00.

[2]  Ken Brodlie,et al.  Improving the Robustness and Accuracy of the Marching Cubes Algorithm for Isosurfacing , 2003, IEEE Trans. Vis. Comput. Graph..

[3]  Michael Garland,et al.  Simplifying surfaces with color and texture using quadric error metrics , 1998, IEEE Visualization.

[4]  David E. Breen,et al.  Semi-regular mesh extraction from volumes , 2000 .

[5]  Bernd Hamann,et al.  The asymptotic decider: resolving the ambiguity in marching cubes , 1991, Proceeding Visualization '91.

[6]  Joe Warren,et al.  Compact Isocontours from sampled Data , 1992, Graphics Gems III.

[7]  Valerio Pascucci,et al.  Fast isocontouring for improved interactivity , 1996, VVS '96.

[8]  David Eppstein,et al.  Provably good mesh generation , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[9]  Herbert Edelsbrunner,et al.  Sliver exudation , 1999, SCG '99.

[10]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[11]  Arie E. Kaufman,et al.  Multiresolution tetrahedral framework for visualizing regular volume data , 1997 .

[12]  Herbert Edelsbrunner,et al.  Sliver exudation , 2000, J. ACM.

[13]  Sarah F. Frisken.  Using Distance Maps for Accurate Surface Representation in Sampled Volumes , 1998, VVS.

[14]  Tao Ju,et al.  Dual contouring of hermite data , 2002, ACM Trans. Graph..

[15]  Valerio Pascucci,et al.  The contour spectrum , 1997, Proceedings. Visualization '97 (Cat. No. 97CB36155).

[16]  Tamal K. Dey,et al.  Quality meshing with weighted Delaunay refinement , 2002, SODA '02.

[17]  Chandrajit L. Bajaj,et al.  Level set based Volumetric Anisotropic Diffusion for 3D Image Filtering , 2003 .

[18]  Jonathan Richard Shewchuk,et al.  What is a Good Linear Element? Interpolation, Conditioning, and Quality Measures , 2002, IMR.

[19]  Vaughan R. Pratt,et al.  Direct least-squares fitting of algebraic surfaces , 1987, SIGGRAPH.

[20]  Rüdiger Westermann,et al.  Real-time exploration of regular volume data by adaptive reconstruction of isosurfaces , 1999, The Visual Computer.

[21]  Mario Botsch,et al.  Feature sensitive surface extraction from volume data , 2001, SIGGRAPH.

[22]  Scott A. Mitchell,et al.  Quality Mesh Generation in Higher Dimensions , 2000, SIAM J. Comput..

[23]  Gregory M. Nielson,et al.  Interval volume tetrahedrization , 1997, Proceedings. Visualization '97 (Cat. No. 97CB36155).

[24]  I. Fujishiro,et al.  Volumetric Data Exploration Using Interval Volume , 1996, IEEE Trans. Vis. Comput. Graph..