Spatial Confidence Regions for Quantifying and Visualizing Registration Uncertainty

  • Takanori Watanabe
  • Clayton Scott
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7359)


For image registration to be applicable in a clinical setting, it is important to know the degree of uncertainty in the returned point-correspondences. In this paper, we propose a data-driven method that allows one to visualize and quantify the registration uncertainty through spatially adaptive confidence regions. The method applies to various parametric deformation models and to any choice of the similarity criterion. We adopt the B-spline model and the negative sum of squared differences for concreteness. At the heart of the proposed method is a novel shrinkage-based estimate of the distribution on deformation parameters. We present some empirical evaluations of the method in 2-D using images of the lung and liver, and the method generalizes to 3-D.


Image Registration Coverage Rate Deformation Model Registration Error Baseline Covariance 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Takanori Watanabe
    • 1
  • Clayton Scott
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of MichiganAnn ArborUSA

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