1 March 2011 A unified framework for voxel classification and triangulation
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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.
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John S. H. Baxter, John S. H. Baxter, Terry M. Peters, Terry M. Peters, Elvis C. S. Chen, Elvis C. S. Chen, } "A unified framework for voxel classification and triangulation", Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 796436 (1 March 2011); doi: 10.1117/12.877715; https://doi.org/10.1117/12.877715
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