Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information.
We propose a joint segmentation and groupwise registration method for dynamic cardiac perfusion images that uses temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registratio...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 2; pp. 173 - 183 |
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| Autor principal: | |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104249475&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104249475 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2013 vid: 26 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104249475 86060008 10.1007/s10278-012-9497-z NLM22688560 PMC3597948 104249475 ppf: 173 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information. aug: au: Mahapatra, Dwarikanath affil: Department of Computer Science, Swiss Federal Institute of Technology (ETH) Zurich, Room CAB F 61.1, Universitätstrasse 6 8092 Zurich Switzerland sug: subj: Perfusion Imaging Image Interpretation, Computer Assisted Methods Contrast Media Algorithms Magnetic Resonance Imaging Methods Comparative Studies Human ab: We propose a joint segmentation and groupwise registration method for dynamic cardiac perfusion images that uses temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registration aims to maximize the smoothness of the intensity signal while segmentation minimizes a pixel's dissimilarity with other pixels having the same segmentation label. The cost function is optimized in an iterative fashion using B-splines. Tests on real patient datasets show that compared with two other methods, our method shows lower registration error and higher segmentation accuracy. This is attributed to the use of temporal information for groupwise registration and mutual complementary registration and segmentation information in one framework while other methods solve the two problems separately. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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