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Gaussian Process Interpolation for Uncertainty Estimation in Image Registration

  • Christian Wachinger
  • Polina Golland
  • Martin Reuter
  • William Wells
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Intensity-based image registration requires resampling images on a common grid to evaluate the similarity function. The uncertainty of interpolation varies across the image, depending on the location of resampled points relative to the base grid. We propose to perform Bayesian inference with Gaussian processes, where the covariance matrix of the Gaussian process posterior distribution estimates the uncertainty in interpolation. The Gaussian process replaces a single image with a distribution over images that we integrate into a generative model for registration. Marginalization over resampled images leads to a new similarity measure that includes the uncertainty of the interpolation. We demonstrate that our approach increases the registration accuracy and propose an efficient approximation scheme that enables seamless integration with existing registration methods.

Keywords

Gaussian Process Image Registration Near Neighbor Uncertainty Estimate Multivariate Gaussian Distribution 
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 2014

Authors and Affiliations

  • Christian Wachinger
    • 1
    • 2
  • Polina Golland
    • 1
  • Martin Reuter
    • 1
    • 2
  • William Wells
    • 1
    • 3
  1. 1.Computer Science and Artificial Intelligence LabMITUSA
  2. 2.Massachusetts General Hospital, Harvard Medical SchoolUSA
  3. 3.Brigham and Women’s Hospital, Harvard Medical SchoolUSA

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