Abstract
A novel groupwise registration framework, called Atlas Building by Self-Organized Registration and Bundling (ABSORB), is proposed in this paper. In this framework, the global structure of relative subject image distribution is preserved during the registration by constraining each subject to deform locally within the learned manifold. A self-organized registration is employed to deform each subject towards a subset of its neighbors that are closer to the global center. Some subjects close enough in the manifold will be bundled into a subgroup during the registration, and then deformed together in the subsequent registration process. This framework performs groupwise registration in a hierarchical way. Specifically, in the higher level, it will perform on a much smaller dataset formed by the representative subjects of all subgroups generated in the previous levels of registration. The atlas image can be eventually built once the registration arrives at the upmost level. Experimental results on both synthetic and real datasets show that the proposed framework can achieve substantial improvements, compared to the other two widely used groupwise methods, in terms of both registration accuracy and robustness.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| Pages | 2785-2790 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2010 Aug 31 |
| Externally published | Yes |
| Event | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United States Duration: 2010 Jun 13 → 2010 Jun 18 |
Other
| Other | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 |
|---|---|
| Country | United States |
| City | San Francisco, CA |
| Period | 10/6/13 → 10/6/18 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
Cite this
ABSORB : Atlas building by self-organized registration and bundling. / Jia, Hongjun; Wu, Guorong; Wang, Qian; Shen, Dinggang.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2010. p. 2785-2790 5540007.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - ABSORB
T2 - Atlas building by self-organized registration and bundling
AU - Jia, Hongjun
AU - Wu, Guorong
AU - Wang, Qian
AU - Shen, Dinggang
PY - 2010/8/31
Y1 - 2010/8/31
N2 - A novel groupwise registration framework, called Atlas Building by Self-Organized Registration and Bundling (ABSORB), is proposed in this paper. In this framework, the global structure of relative subject image distribution is preserved during the registration by constraining each subject to deform locally within the learned manifold. A self-organized registration is employed to deform each subject towards a subset of its neighbors that are closer to the global center. Some subjects close enough in the manifold will be bundled into a subgroup during the registration, and then deformed together in the subsequent registration process. This framework performs groupwise registration in a hierarchical way. Specifically, in the higher level, it will perform on a much smaller dataset formed by the representative subjects of all subgroups generated in the previous levels of registration. The atlas image can be eventually built once the registration arrives at the upmost level. Experimental results on both synthetic and real datasets show that the proposed framework can achieve substantial improvements, compared to the other two widely used groupwise methods, in terms of both registration accuracy and robustness.
AB - A novel groupwise registration framework, called Atlas Building by Self-Organized Registration and Bundling (ABSORB), is proposed in this paper. In this framework, the global structure of relative subject image distribution is preserved during the registration by constraining each subject to deform locally within the learned manifold. A self-organized registration is employed to deform each subject towards a subset of its neighbors that are closer to the global center. Some subjects close enough in the manifold will be bundled into a subgroup during the registration, and then deformed together in the subsequent registration process. This framework performs groupwise registration in a hierarchical way. Specifically, in the higher level, it will perform on a much smaller dataset formed by the representative subjects of all subgroups generated in the previous levels of registration. The atlas image can be eventually built once the registration arrives at the upmost level. Experimental results on both synthetic and real datasets show that the proposed framework can achieve substantial improvements, compared to the other two widely used groupwise methods, in terms of both registration accuracy and robustness.
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U2 - 10.1109/CVPR.2010.5540007
DO - 10.1109/CVPR.2010.5540007
M3 - Conference contribution
AN - SCOPUS:77955988358
SN - 9781424469840
SP - 2785
EP - 2790
BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ER -