Statistically-constrained high-dimensional warping using wavelet-based priors

Zhong Xue, Dinggang Shen, Christos Davatzikos

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

2 Scopus citations


In this paper, a Statistical Model of Deformation (SMD) that captures the statistical prior distribution of high-dimensional deformations more accurately and effectively than conventional PCA-based statistical shape models is used to regularize deformable registration. SMD utilizes a wavelet-based representation of statistical variation of a deformation field and its Jacobian, and it is able to capture both global and fine shape detail without overconstraining the deformation process. This approach is shown to produce more accurate and robust registration results in MR brain images, relative to the registration methods that use Laplacian-based smoothness constraints of deformation fields. In experiments, we evaluate the SMD-constrained registration by comparing the performance of registration with and without SMD in a specific deformable registration framework. The proposed method can potentially incorporate various registration algorithms to improve their robustness and stability using statistically-based regularization.

Original languageEnglish (US)
Title of host publication2006 Conference on Computer Vision and Pattern Recognition Workshop
StatePublished - 2006
Event2006 Conference on Computer Vision and Pattern Recognition Workshops - New York, NY, United States
Duration: Jun 17 2006Jun 22 2006

Publication series

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


Other2006 Conference on Computer Vision and Pattern Recognition Workshops
Country/TerritoryUnited States
CityNew York, NY

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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