ABSORB: Atlas building by self-organized registration and bundling

Hongjun Jia, Guorong Wu, Qian Wang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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 languageEnglish
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Pages2785-2790
Number of pages6
DOIs
Publication statusPublished - 2010
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United States
Duration: 2010 Jun 132010 Jun 18

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
CountryUnited States
CitySan Francisco, CA
Period10/6/1310/6/18

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Jia, H., Wu, G., Wang, Q., & Shen, D. (2010). ABSORB: Atlas building by self-organized registration and bundling. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 (pp. 2785-2790). [5540007] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2010.5540007