Statistically-constrained deformable registration of MR brain images

Zhong Xue, Dinggang Shen

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

8 Citations (Scopus)


Statistical models of deformations (SMD) capture the variability of deformations of a group of sample images, and they are often used to constrain deformable registration, thereby improving their robustness and accuracy. Although low- dimensional statistical models, such as active shape and appearance models, have been successfully used in statistically-constrained deformable models, constraining of high- dimensional warping algorithms is a more challenging task, since conventional PCA-based statistics are limited to capture the full range of anatomical variability. This paper first proposes an SMD that is built upon the wavelet-PCA model and then uses it to constrain the deformable registration, wherein the template image is adaptively warped based on SMD during the registration procedure. Compared to the original template image, the adaptively deformed template image is more similar to the subject image, e.g., the deformation is relatively small and local, and it is less likely to be stuck in undesired local minima. In experiments, we show that the proposed statistically-constrained deformable registration is more robust and accurate than the conventional registration.

Original languageEnglish
Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
Number of pages4
Publication statusPublished - 2007
Externally publishedYes
Event2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States
Duration: 2007 Apr 122007 Apr 15

Publication series

Name2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings


Other2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
Country/TerritoryUnited States
CityArlington, VA


  • Biomedical image processing
  • Image registration
  • Magnetic resonance imaging
  • Statistics

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Medicine


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