Robust CT to US 3D-3D Registration by Using Principal Component Analysis and Kalman Filtering

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dc.contributor.authorEcheverría, Rebeca
dc.contributor.authorCortes, Camilo
dc.contributor.authorBertelsen, Alvaro
dc.contributor.authorMacia, Ivan
dc.contributor.authorRuíz, Óscar E.
dc.contributor.authorFlórez, Julián
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería Mecánicaspa
dc.contributor.researchgroupLaboratorio CAD/CAM/CAEspa
dc.date.accessioned2016-11-30T15:56:58Z
dc.date.available2016-11-30T15:56:58Z
dc.date.issued2016-07
dc.description.abstractAlgorithms based on the unscented Kalman filter (UKF) have been proposed as an alternative for registration of point clouds obtained from vertebral ultrasound (US) and computerised tomography (CT) scans, effectively handling the US limited depth and low signaltonoise ratio -- Previously proposed methods are accurate, but their convergence rate is considerably reduced with initial misalignments of the datasets greater than or 30 mm -- We propose a novel method which increases robustness by adding a coarse alignment of the datasets’ principal components and batchbased point inclusions for the UKF -- Experiments with simulated scans with full coverage of a single vertebra show the method’s capability and accuracy to correct misalignments as large as and 90 mm -- Furthermore, the method registers datasets with varying degrees of missing data and datasets with outlier points coming from adjacent vertebraeeng
dc.description.notepp.52 - 63spa
dc.formatapplication/pdfeng
dc.identifier.citation@Inbook{Echeverri2016, author={Echeverria, Rebeca and Cortes, Camilo and Bertelsen, Alvaro and Macia, Ivan and Ruiz, Oscar E. and Florez, Julian}, editor={Vrtovec, Tomaz and Yao, Jianhua and Glocker, Ben and Klinder, Tobias and Frangi, Alejandro and Zheng, Guoyan and Li, Shuo}, title={Robust CT to US 3D-3D Registration by Using Principal Component Analysis and Kalman Filtering}, bookTitle={Computational Methods and Clinical Applications for Spine Imaging: Third International Workshop and Challenge, CSI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015, Proceedings}, year={2016}, publisher={Springer International Publishing}, address={Cham}, pages={52--63}, isbn={978-3-319-41827-8}, doi={10.1007/978-3-319-41827-8_5}, url={http://dx.doi.org/10.1007/978-3-319-41827-8_5}spa
dc.identifier.doi10.1007/978-3-319-41827-8_5
dc.identifier.urihttp://hdl.handle.net/10784/9793
dc.language.isoengspa
dc.relation.ispartofComputational Methods and Clinical Applications for Spine Imagingspa
dc.relation.isversionofhttps://doi.org/10.1007/978-3-319-41827-8_5spa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccesseng
dc.rights.localAcceso cerradospa
dc.subject.keywordTomographyeng
dc.subject.keywordKalman filteringeng
dc.subject.keywordUltrasonics in medicineeng
dc.subject.keywordMathematical modelseng
dc.subject.keywordDifferential equationseng
dc.subject.keywordImage processingeng
dc.subject.keywordNube de puntosspa
dc.subject.keywordImagen médica multimodalspa
dc.subject.keywordMétodos computacionalesspa
dc.subject.lembTOMOGRAFÍAspa
dc.subject.lembFILTRACIÓN KALMANspa
dc.subject.lembULTRASONIDO EN MEDICINAspa
dc.subject.lembMODELOS MATEMÁTICOSspa
dc.subject.lembECUACIONES DIFERENCIALESspa
dc.subject.lembPROCESAMIENTO DE IMÁGENESspa
dc.titleRobust CT to US 3D-3D Registration by Using Principal Component Analysis and Kalman Filteringeng
dc.typeinfo:eu-repo/semantics/bookParteng
dc.typebookParteng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.hasVersionObra publicadaspa
dc.type.localCapítulo o parte de un librospa

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