A unified framework for voxel classification and triangulation
Published in SPIE Medical Imaging, 2011
Recommended citation: Baxter JS, Peters TM, Chen ECS, (2011). "A unified framework for voxel classification and triangulation"; in SPIE Medical Imaging: Visualization, Image-Guided Procedures, and Modeling, 796436, pp. 933-940. https://doi.org/10.1117/12.877715
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.