Paper Info
Title
A unified framework for voxel classification and triangulation
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.
Year
DOI
Venue
2011
10.1117/12.877715
Proceedings of SPIE
Keywords
Field
DocType
2D Classification and Transfer Function,Volume Rendering,Triangulation,GPU
Voxel,Computer vision,Visual artifact,Volume rendering,Polygon mesh,Computer science,Triangulation (social science),Artificial intelligence,Anatomical structures,Linear regression,Visual appearance
Conference
Volume
ISSN
Citations 
7964
0277-786X
2
PageRank 
References 
Authors
0.41
0
3
Name
Order
Citations
PageRank
John S. H. Baxter17414.67
Terry M. Peters21335181.71
Elvis C. S. Chen39522.10