TY - GEN
T1 - A tightly coupled region-shape framework for 3D medical image segmentation
AU - Huang, Rui
AU - Pavlovic, Vladimir
AU - Metaxas, Dimitris N.
PY - 2006
Y1 - 2006
N2 - Most hybrid 3D segmentation methods either heuristically couple the respective algorithm or combine a true 3D with a 2D algorithm due to computational considerations. In this paper we propose a new probabilistic framework for 3D image segmentation that combines tightly linked region- and shape-based constraints. Regionbased label constraints are modeled by a 3D Markov random field, and are tightly coupled to shape-based constraints of a 3D Deformable Model. The full 3D nature of the combined model leads to a robust smooth surface segmentation that outperforms the single constraint, slice-based as well as the loosely coupled 3D methods.
AB - Most hybrid 3D segmentation methods either heuristically couple the respective algorithm or combine a true 3D with a 2D algorithm due to computational considerations. In this paper we propose a new probabilistic framework for 3D image segmentation that combines tightly linked region- and shape-based constraints. Regionbased label constraints are modeled by a 3D Markov random field, and are tightly coupled to shape-based constraints of a 3D Deformable Model. The full 3D nature of the combined model leads to a robust smooth surface segmentation that outperforms the single constraint, slice-based as well as the loosely coupled 3D methods.
UR - http://www.scopus.com/inward/record.url?scp=33750949040&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33750949040&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33750949040
SN - 0780395778
SN - 9780780395770
T3 - 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings
SP - 426
EP - 429
BT - 2006 3rd IEEE International Symposium on Biomedical Imaging
T2 - 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Y2 - 6 April 2006 through 9 April 2006
ER -