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dc.contributor.authorCorona, Veronica
dc.contributor.authorBenning, Martin
dc.contributor.authorEhrhardt, Matthias
dc.contributor.authorGladden, Lynn
dc.contributor.authorMair, R
dc.contributor.authorReci, A
dc.contributor.authorSederman, Andy
dc.contributor.authorReichelt, Stefanie
dc.contributor.authorSchönlieb, CB
dc.date.accessioned2019-04-02T12:36:15Z
dc.date.available2019-04-02T12:36:15Z
dc.date.issued2019-05
dc.identifier.issn0266-5611
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/291045
dc.description.abstractAll imaging modalities such as computed tomography (CT), emission tomography and magnetic resonance imaging (MRI) require a reconstruction approach to produce an image. A common image processing task for applications that utilise those modalities is image segmentation, typically performed posterior to the reconstruction. We explore a new approach that combines reconstruction and segmentation in a unified framework. We derive a variational model that consists of a total variation regularised reconstruction from undersampled measurements and a Chan-Vese based segmentation. We extend the variational regularisation scheme to a Bregman iteration framework to improve the reconstruction and therefore the segmentation. We develop a novel alternating minimisation scheme that solves the non-convex optimisation problem with provable convergence guarantees. Our results for synthetic and real data show that both reconstruction and segmentation are improved compared to the classical sequential approach.
dc.publisherIOP Publishing
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleEnhancing joint reconstruction and segmentation with non-convex Bregman iteration
dc.typeArticle
prism.issueIdentifier5
prism.publicationDate2019
prism.publicationNameInverse Problems
prism.volume35
dc.identifier.doi10.17863/CAM.38225
dcterms.dateAccepted2019-02-28
rioxxterms.versionofrecord10.1088/1361-6420/ab0b77
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-04-26
dc.contributor.orcidCorona, Veronica [0000-0003-2160-5482]
dc.contributor.orcidBenning, Martin [0000-0002-6203-1350]
dc.contributor.orcidEhrhardt, Matthias [0000-0001-8523-353X]
dc.contributor.orcidGladden, Lynn [0000-0001-9519-0406]
dc.contributor.orcidSederman, Andy [0000-0002-7866-5550]
dc.contributor.orcidReichelt, Stefanie [0000-0003-4151-0712]
dc.identifier.eissn1361-6420
dc.publisher.urlhttp://dx.doi.org/10.1088/1361-6420/ab0b77
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/M00483X/1)
pubs.funder-project-idLeverhulme Trust (RPG-2015-250)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/H023348/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N014588/1)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (691070)
pubs.funder-project-idAlan Turing Institute (unknown)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (777826)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/J009539/1)
cam.issuedOnline2019-04-26
cam.orpheus.successThu Jan 30 10:49:10 GMT 2020 - Embargo updated
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International