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dc.contributor.authorHolden, Matthew
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2014-08-25 10:35:42.122en
dc.date.accessioned2014-08-25T18:38:52Z
dc.date.available2014-08-25T18:38:52Z
dc.date.issued2014-08-25
dc.identifier.urihttp://hdl.handle.net/1974/12373
dc.descriptionThesis (Master, Computing) -- Queen's University, 2014-08-25 10:35:42.122en
dc.description.abstractPurpose: Image-guided interventions rely on registration of images, models, and surgical tools into a common navigation space. Point-set registration is commonly used to perform this registration, but requires well-defined landmark points to be present on these tools. In this thesis, a generalization of point-set registration is proposed to simultaneously register point, line, and plane landmarks present on surgical tools. This facilitates registration when point-set registration is not feasible. Methods: The proposed algorithm first determines correspondences between points, lines, and planes in the coordinate systems using a set of “reference” landmarks, then calculates invariant features in each coordinate frame for an initial registration, and finally optimizes the registration iteratively. Several forms of validation are investigated: registration of simulated data with a known ground-truth registration, phantom registration using a tracked stylus for registration to a model or a volume, and volume registration of a reconstructed ultrasound volume to a model. Validation accuracy is determined by comparison to a known ground-truth registration or using registration quality metrics such as target registration error. Results: For the simulated data experiments, the linear object registration was sufficiently close to the ground-truth registration in all cases given the level of noise. For real registration experiments, in all instances where accurate point-set registration was possible, the linear object registration was equally as accurate, and the difference between the two registrations was less than the fiducial localization error. When accurate point-set registration was not possible, the linear object registration was observed to be more accurate and more precise than point-set registration using approximate landmarks. Conclusion: The proposed linear object registration algorithm is a viable alternative when point-set registration cannot be performed. The algorithm has been developed as an open-source registration tool for practical use as a module for the 3D Slicer platform.en_US
dc.languageenen
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectRegistrationen_US
dc.subjectCoordinate Transformationsen_US
dc.subjectSurgical Navigationen_US
dc.titleLinear Object Registration for Image-Guided Interventionsen_US
dc.typeThesisen_US
dc.description.degreeMasteren
dc.contributor.supervisorFichtinger, Gaboren
dc.contributor.departmentComputingen


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