A General Learning Framework for Non-rigid Image Registration

  • Guorong Wu
  • Feihu Qi
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)


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.


Good Feature Image Registration Deformation Field Registration Algorithm Jacobian Determinant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guorong Wu
    • 1
  • Feihu Qi
    • 1
  • Dinggang Shen
    • 2
  1. 1.Depart. of Computer Science and EngineeringShanghai Jiao Tong UniversityChina
  2. 2.Section of Biomedical Image Analysis, Depart. of RadiologyUniversity of Pennsylvania 

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