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Proceedings Paper

A unified framework for voxel classification and triangulation
Author(s): John S. H. Baxter; Terry M. Peters; Elvis C. S. Chen
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Paper Abstract

A unified framework for voxel classification and triangulation for medical images is presented. Given volumetric data, each voxel is labeled by a two-dimensional classification function based on voxel intensity and gradient. A modified Constrained Elastic Surface Net is integrated into the classification function, allowing the surface mesh to be generated in a single step. The modification to the Constrained Elastic Surface Net includes additional triangulation cases which reduce visual artifacts, and a surface-node relaxation criterion based on linear regression which improves visual appearance and preserves the enclosed volume. By carefully designing the two-dimensional classification function, surface meshes for different anatomical structures can be generated in a single process. This framework is implemented on the GPU, allowing rendition of the voxel classification to be visualized in near real-time.

Paper Details

Date Published: 2 March 2011
PDF: 8 pages
Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 796436 (2 March 2011); doi: 10.1117/12.877715
Show Author Affiliations
John S. H. Baxter, Univ. of Waterloo (Canada)
Terry M. Peters, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Elvis C. S. Chen, Robarts Research Institute (Canada)


Published in SPIE Proceedings Vol. 7964:
Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling
Kenneth H. Wong; David R. Holmes, Editor(s)

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