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dc.contributor.authorPeroni, M.
dc.contributor.authorSharp, G. C.
dc.contributor.authorBaroni, G.
dc.contributor.authorGolland, Polina
dc.date.accessioned2017-08-18T17:52:57Z
dc.date.available2017-08-18T17:52:57Z
dc.date.issued2013-08
dc.date.submitted2013-06
dc.identifier.issn1533-0346
dc.identifier.issn1533-0338
dc.identifier.urihttp://hdl.handle.net/1721.1/110986
dc.description.abstractA crucial issue in deformable image registration is achieving a robust registration algorithm at a reasonable computational cost. Given the iterative nature of the optimization procedure an algorithm must automatically detect convergence, and stop the iterative process when most appropriate. This paper ranks the performances of three stopping criteria and six stopping value computation strategies for a Log-Domain Demons Deformable registration method simulating both a coarse and a fine registration. The analyzed stopping criteria are: (a) velocity field update magnitude, (b) mean squared error, and (c) harmonic energy. Each stoping condition is formualted so that the user defines a threshold ∊, which quantifies the residual error that is acceptable for the particular problem and calculation strategy. In this work, we did not aim at assigning a value to e, but to give insights in how to evaluate and to set the threshold on a given exit strategy in a very popular registration scheme. Experi-ments on phantom and patient data demonstrate that comparing the optimization metric minimum over the most recent three iterations with the minimum over the fourth to sixth most recent iterations can be an appropriate algorithm stopping strategy. The harmonic energy was found to provide best trade-off between robustness and speed of convergence for the analyzed registration method at coarse registration, but was outperformed by mean squared error when all the original pixel information is used. This suggests the need of developing mathematically sound new convergence criteria in which both image and vector field information could be used to detect the actual convergence, which could be especially useful when considering multi-resolution registrations. Further work should be also dedicated to study same strategies performances in other deformable registration methods and body districts.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant 2-U54EB005149-06)en_US
dc.language.isoen_US
dc.publisherSAGE Publicationsen_US
dc.relation.isversionofhttp://dx.doi.org/10.7785/tcrtexpress.2013.600269en_US
dc.rightsCreative Commons Attribution-NonCommercial 3.0 Unporteden_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/en_US
dc.sourceSageen_US
dc.titleStopping Criteria for Log-Domain Diffeomorphic Demons Registrationen_US
dc.typeArticleen_US
dc.identifier.citationPeroni, M. et al. “Stopping Criteria for Log-Domain Diffeomorphic Demons Registration.” Technology in Cancer Research & Treatment 15, 1 (February 2016): 77–90 © 2014 The Authorsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorGolland, Polina
dc.relation.journalTechnology in Cancer Research & Treatmenten_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsPeroni, M.; Golland, P.; Sharp, G. C.; Baroni, G.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-2516-731X
mit.licensePUBLISHER_CCen_US


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