Learning best features and deformation statistics for hierarchical registration of MR brain images

Guorong Wu, Feihu Qi, Dinggang Shen

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

Abstract

A fully learning-based framework has been presented for deformable registration of MR brain images. In this framework, the entire brain is first adaptively partitioned into a number of brain regions, and then the best features are learned for each of these brain regions. In order to obtain overall better performance for both of these two steps, they are integrated into a single framework and solved together by iteratively performing region partition and learning the best features for each partitioned region. In particular, the learned best features for each brain region are required to be identical, and maximally salient as well as consistent over all individual brains, thus facilitating the correspondence detection between individual brains during the registration procedure. Moreover, the importance of each brain point in registration is evaluated according to the distinctiveness and consistency of its respective best features, therefore the salient points with distinctive and consistent features can be hierarchically selected to steer the registration process and reduce the risk of being trapped in local minima. Finally, the statistics of inter-brain deformations, represented by multi-level B-Splines, is also hierarchically captured for effectively constraining the brain deformations estimated during the registration procedure. By using this proposed learning-based registration framework, more accurate and robust registration results can be achieved according to experiments on both real and simulated data.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages160-171
Number of pages12
Volume4584 LNCS
Publication statusPublished - 2007 Dec 1
Externally publishedYes
Event20th International Conference on Information Processing in Medical lmaging, IPMI 2007 - Kerkrade, Netherlands
Duration: 2007 Jul 22007 Jul 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4584 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other20th International Conference on Information Processing in Medical lmaging, IPMI 2007
CountryNetherlands
CityKerkrade
Period07/7/207/7/6

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ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Wu, G., Qi, F., & Shen, D. (2007). Learning best features and deformation statistics for hierarchical registration of MR brain images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4584 LNCS, pp. 160-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4584 LNCS).