TY - GEN
T1 - A deformable cosegmentation algorithm for brain MR images
AU - Zhang, Tong
AU - Xia, Yong
AU - Feng, David Dagan
PY - 2012
Y1 - 2012
N2 - Cosegmentation aims to simultaneously segment the common parts in a pair of images, and has recently attracted increasing research attention in the field of computer vision. In this paper, we propose a novel deformable cosegmentation (D-C) algorithm to solve the brain MR image segmentation problem by cosegmenting the image and a co-registered atlas. In this manner, the prior heuristic information about brain anatomy that is embedded in the atlas can be transformed into the constraints that control the segmentation of brain MR images. Based on the multiphase Chan-Vese model, the proposed D-C algorithm is implemented using level set techniques. Then, it is compared to the protocol algorithm and the state-of-the-art GA-EM algorithm in T1-weighted brain MR images corrupted by different levels of Gaussian noise and intensity non-uniformity. Our results show that the proposed D-C algorithm can differentiate major brain structures more accuratly and produce more robust segmentation of brain MR images.
AB - Cosegmentation aims to simultaneously segment the common parts in a pair of images, and has recently attracted increasing research attention in the field of computer vision. In this paper, we propose a novel deformable cosegmentation (D-C) algorithm to solve the brain MR image segmentation problem by cosegmenting the image and a co-registered atlas. In this manner, the prior heuristic information about brain anatomy that is embedded in the atlas can be transformed into the constraints that control the segmentation of brain MR images. Based on the multiphase Chan-Vese model, the proposed D-C algorithm is implemented using level set techniques. Then, it is compared to the protocol algorithm and the state-of-the-art GA-EM algorithm in T1-weighted brain MR images corrupted by different levels of Gaussian noise and intensity non-uniformity. Our results show that the proposed D-C algorithm can differentiate major brain structures more accuratly and produce more robust segmentation of brain MR images.
KW - deformable model
KW - Image cosegmentation
KW - Magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=84882995806&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2012.6346649
DO - 10.1109/EMBC.2012.6346649
M3 - 会议稿件
C2 - 23366610
AN - SCOPUS:84882995806
SN - 9781424441198
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3215
EP - 3218
BT - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
T2 - 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Y2 - 28 August 2012 through 1 September 2012
ER -