Gaussian Process Interpolation for Uncertainty Estimation in Image Registration

• Main Authors: Wachinger, Christian (Contributor), Golland, Polina (Contributor), Reuter, Martin (Contributor), Wells, William M. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Springer-Verlag, 2015-12-15T15:23:57Z.
• Subjects: Article
• Online Access: Get fulltext

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.
Alexander von Humboldt-Stiftung
National Alliance for Medical Image Computing (U.S.) (U54-EB005149)
Neuroimaging Analysis Center (U.S.) (P41-EB015902)
National Center for Image-Guided Therapy (U.S.) (P41-EB015898)