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3D-2D image registration by nonlinear regression

dc.contributor.authorGouveia, A. R.
dc.contributor.authorMetz, C.
dc.contributor.authorFreire, Luís
dc.contributor.authorKlein, S.
dc.date.accessioned2013-12-23T18:56:20Z
dc.date.available2013-12-23T18:56:20Z
dc.date.issued2012
dc.description.abstractWe propose a 3D-2D image registration method that relates image features of 2D projection images to the transformation parameters of the 3D image by nonlinear regression. The method is compared with a conventional registration method based on iterative optimization. For evaluation, simulated X-ray images (DRRs) were generated from coronary artery tree models derived from 3D CTA scans. Registration of nine vessel trees was performed, and the alignment quality was measured by the mean target registration error (mTRE). The regression approach was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach.por
dc.identifier.citationGouveia AR, Metz C, Freire L, Klein S. 3D-2D image registration by nonlinear regression. In 9th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE; 2012. p. 1343-6.por
dc.identifier.isbn978-1-4577-1857-1
dc.identifier.urihttp://hdl.handle.net/10400.21/3029
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherIEEEpor
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6235814&queryText%3D3D-2D+image+registration+by+nonlinear+regressionpor
dc.subject2D/3D Image registrationpor
dc.subjectImage guided interventionspor
dc.subjectRegressionpor
dc.subjectFeature extractionpor
dc.subjectImage registrationpor
dc.subjectNeural networkspor
dc.subjectOptimizationpor
dc.subjectRobustnesspor
dc.subjectTrainingpor
dc.subjectX-ray imagingpor
dc.title3D-2D image registration by nonlinear regressionpor
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage1346por
oaire.citation.startPage1343por
rcaap.rightsrestrictedAccesspor
rcaap.typebookPartpor

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