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dc.contributor.authorRisholm, Petter
dc.contributor.authorJanoos, Firdaus
dc.contributor.authorNorton, Isaiah Hakim
dc.contributor.authorGolby, Alexandra Jacqueline
dc.contributor.authorWells, William Mercer
dc.date.accessioned2017-11-08T20:19:47Z
dc.date.issued2013
dc.identifierQuick submit: 2013-11-15T12:16:51-05:00
dc.identifier.citationRisholm, 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/j.media.2013.03.002.en_US
dc.identifier.issn1361-8415en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:34341843
dc.description.abstractIn 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.en_US
dc.language.isoen_USen_US
dc.publisherElsevier BVen_US
dc.relation.isversionofdoi:10.1016/j.media.2013.03.002en_US
dc.relation.hasversionhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3687087/en_US
dash.licenseLAA
dc.titleBayesian characterization of uncertainty in intra-subject non-rigid registrationen_US
dc.typeJournal Articleen_US
dc.date.updated2013-11-15T17:18:02Z
dc.description.versionAccepted Manuscripten_US
dc.rights.holderRisholm P, Janoos F, Norton I, Golby AJ, Wells WM 3rd.
dc.relation.journalMedical Image Analysisen_US
dash.depositing.authorGolby, Alexandra Jacqueline
dc.date.available2017-11-08T20:19:47Z
dc.identifier.doi10.1016/j.media.2013.03.002*
workflow.legacycommentsaa.no Golby emailed for aa 04-25-2017 MMen_US
dash.contributor.affiliatedGolby, Alexandra
dash.contributor.affiliatedNorton, Isaiah Hakim
dash.contributor.affiliatedWells, William


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