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

  • Authors:

  • Guorong Wu;Feihu Qi;Dinggang Shen

  • Affiliations:

  • Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China and Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philad ...;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA

  • Venue:
  • IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
  • Year:
  • 2007

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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.