Improving Probabilistic Image Registration via Reinforcement Learning and Uncertainty Evaluation

  • Tayebeh Lotfi
  • Lisa Tang
  • Shawn Andrews
  • Ghassan Hamarneh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)


One framework for probabilistic image registration involves assigning probability distributions over spatial transformations (e.g. distributions over displacement vectors at each voxel). In this paper, we propose an uncertainty measure for these distributions that examines the actual spatial displacements, thus departing from the classical Shannon entropy-based measures, which examine only the probabilities of these distributions. We show that by incorporating the proposed uncertainty measure, along with features extracted from the input images and intermediate displacement fields, we are able to more accurately predict the pointwise registration errors of an intermediate solution as estimated for a previously unseen input image pair. We utilize the predicted errors to identify regions in the image that are trustworthy and through which we refine the tentative registration solution. Results show that our proposed framework, which incorporates uncertainty estimation and registration error prediction, can improve accuracy of 3D image registrations by about 25%.


Uncertainty Measure Image Registration Registration Error Target Registration Error Deformable Image Registration 
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 International Publishing Switzerland 2013

Authors and Affiliations

  • Tayebeh Lotfi
    • 1
  • Lisa Tang
    • 1
  • Shawn Andrews
    • 1
  • Ghassan Hamarneh
    • 1
  1. 1.Medical Image Analysis Lab.Simon Fraser UniversityBurnabyCanada

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