Gaussian Process Interpolation for Uncertainty Estimation in Image Registration
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Golland_Gaussian process.pdf
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Author(s)
Wachinger, Christian
Golland, Polina
Reuter, Martin
Wells, William M.
Date Issued
2014
Journal
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014
Publisher
Springer-Verlag
Citation
Wachinger, Christian, Polina Golland, Martin Reuter, and William Wells. “Gaussian Process Interpolation for Uncertainty Estimation in Image Registration.” Lecture Notes in Computer Science (2014): 267–274.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-10404-1_34
