Bayesian characterization of uncertainty in intra-subject non-rigid registration

DSpace/Manakin Repository

Bayesian characterization of uncertainty in intra-subject non-rigid registration

Citable link to this page


Title: Bayesian characterization of uncertainty in intra-subject non-rigid registration
Author: Risholm, Petter; Janoos, Firdaus; Norton, Isaiah Hakim; Golby, Alexandra Jacqueline; Wells, William Mercer

Note: Order does not necessarily reflect citation order of authors.

Citation: Risholm, Petter, Firdaus Janoos, Isaiah Norton, Alex J. Golby, and William M. Wells. 2013. “Bayesian Characterization of Uncertainty in Intra-Subject Non-Rigid Registration.” Medical Image Analysis 17 (5) (July): 538–555. doi:10.1016/
Full Text & Related Files:
Abstract: In settings where high-level inferences are made based on registered image data, the registration uncertainty can contain important information. In this article, we propose a Bayesian non-rigid registration framework where conventional dissimilarity and regularization energies can be included in the likelihood and the prior distribution on deformations respectively through the use of Boltzmann’s distribution. The posterior distribution is characterized using Markov Chain Monte Carlo (MCMC) methods with the effect of the Boltzmann temperature hyper-parameters marginalized under broad uninformative hyper-prior distributions. The MCMC chain permits estimation of the most likely deformation as well as the associated uncertainty. On synthetic examples, we demonstrate the ability of the method to identify the maximum a posteriori estimate and the associated posterior uncertainty, and demonstrate that the posterior distribution can be non-Gaussian. Additionally, results from registering clinical data acquired during neurosurgery for resection of brain tumor are provided; we compare the method to single transformation results from a deterministic optimizer and introduce methods that summarize the high-dimensional uncertainty. At the site of resection, the registration uncertainty increases and the marginal distribution on deformations is shown to be multi-modal.
Published Version: doi:10.1016/
Other Sources:
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at
Citable link to this page:
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)


Search DASH

Advanced Search