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dc.contributor.authorZheng, Chaojie
dc.contributor.authorWang, Xiuying
dc.contributor.authorZeng, Shan
dc.contributor.authorZhou, Jianlong
dc.contributor.authorYin, Yong
dc.contributor.authorFeng, Dagan
dc.contributor.authorFulham, Michael
dc.date.accessioned2022-12-20T03:56:20Z
dc.date.available2022-12-20T03:56:20Z
dc.date.issued2018en_AU
dc.identifier.urihttps://hdl.handle.net/2123/29820
dc.description.abstractThe demons registration (DR) model is well recognized for its deformation capability. However, it might lead to misregistration due to erroneous diffusion direction when there are no overlaps between corresponding regions. We propose a novel registration energy function, introducing topology energy, and incorporating a local energy function into the DR in a progressive registration scheme, to address these shortcomings. The topology energy that is derived from the topological information of the images serves as a direction inference to guide diffusion transformation to retain the merits of DR. The local energy constrains the deformation disparity of neighbouring pixels to maintain important local texture and density features. The energy function is minimized in a progressive scheme steered by a topology tree graph and we refer to it as topology-guided deformable registration (TDR). We validated our TDR on 20 pairs of synthetic images with Gaussian noise, 20 phantom PET images with artificial deformations and 12 pairs of clinical PET-CT studies. We compared it to three methods: (1) free-form deformation registration method, (2) energy-based DR and (3) multi-resolution DR. The experimental results show that our TDR outperformed the other three methods in regard to structural correspondence and preservation of the local important information including texture and density, while retaining global correspondence.en_AU
dc.language.isoenen_AU
dc.publisherIOP Publishingen_AU
dc.relation.ispartofPhysics in Medicine & Biologyen_AU
dc.subjectdeformable registrationen_AU
dc.subjecttopology guidanceen_AU
dc.subjectlocal importance preservationen_AU
dc.subjectdemonsen_AU
dc.subjecttopology treeen_AU
dc.titleTopology-guided deformable registration with local importance preservation for biomedical imagesen_AU
dc.typeArticleen_AU
dc.subject.asrc0801 Artificial Intelligence and Image Processingen_AU
dc.identifier.doi10.1088/1361-6560/aa9917
dc.type.pubtypePublisher's versionen_AU
usyd.facultySeS faculties schools::Faculty of Engineering::School of Computer Scienceen_AU
usyd.citation.volume63en_AU
usyd.citation.issue015028en_AU
usyd.citation.spage1en_AU
usyd.citation.epage15en_AU
workflow.metadata.onlyNoen_AU


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