A general learning framework for non-rigid image registration

Guorong Wu, Feihu Qi, Dinggang Shen

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

7 Citations (Scopus)


This paper presents a general learning framework for non-rigid registration of MR brain images. Given a set of training MR brain images, three major types of information are particularly learned, and further incorporated into a HAMMER registration algorithm for improving the performance of registration. First, the best features are learned from different types of local image descriptors for each part of brain, thereby the learned best features are consistent on the correspondence points across individual brains, but different on non-correspondence points. Moreover, the statistics of selected best features is learned from the training samples, and used to guide the feature matching during the image registration. Second, in order to avoid the local minima in the registration, the points hierarchically selected to drive image registration are determined by the learned consistency and distinctiveness of their respective best features. Third, deformation fields are adaptively represented by B-splines, with more control points placed on the regions with large shape variations across individual brains or on the regions with consistent and distinctive best features. Also, the statistics of B-splines based deformations is captured and used to regularize the brain registration. Finally, by incorporating all learned information into HAMMER registration framework, promising results are obtained on both real and simulated data.

Original languageEnglish
Title of host publicationMedical Imaging and Augmented Reality - Third International Workshop
PublisherSpringer Verlag
Number of pages9
ISBN (Print)3540372202, 9783540372202
Publication statusPublished - 2006
Externally publishedYes
Event3rd International Workshop on Medical Imaging and Augmented Reality - Shanghai, China
Duration: 2006 Aug 172006 Aug 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4091 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd International Workshop on Medical Imaging and Augmented Reality

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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