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dc.contributor.advisorBlake, Richard E.nb_NO
dc.contributor.advisorLindseth, Franknb_NO
dc.contributor.authorRyen, Tommynb_NO
dc.date.accessioned2014-12-19T13:31:31Z
dc.date.available2014-12-19T13:31:31Z
dc.date.created2010-09-02nb_NO
dc.date.issued2006nb_NO
dc.identifier347335nb_NO
dc.identifierntnudaim:1387nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/250380
dc.description.abstractThis master-thesis considers implementation of automated multimodal volume-to-volume registration of images, in order to provide neurosurgeons with valuable information for planning and intraoperative guidance. Focus has been on medical images from magnetic resonance (MR) and ultrasound (US) for use in surgical guidance. Prototype implementations for MRI-to-US registration have been proposed, and tested, using registration methods available in the Insight Toolkit (ITK). Mattes' Mutual Information has been the similarty metric, based on unpreprocessed angio-graphic volumes from both modalities. Only rigid transformations has been studied, and both types of Gradient Descent and Evolutionary optimizers has been examinated. The applications have been tested on clinical data from relevant surgical operations. The best results were obtained using an evolutional (1+1) optimizer for translational transformations only. This application was both fast and accurate. The other applications, using types of Gradient Descent optimizers, has proved to be significantly slower, inaccurate and more difficult to parameterize. It has been experienced that registration of angio-graphic volumes are easier to accomplish than registration of volumes of other weightings, due to their more similar characteristics. Angio-graphic images are also readily evaluated using volume renderings, but other methods should be constructed to provide a less subjective measure of success for the registration procedures. The obtained results indicate that automatic volume-to-volume registration of angio-graphic images from MRI and US, using Mattes' Mutual Information and an Evolutionary Optimizer, should be feasible for the neuronavigational system considered here, with sufficient accuracy. Further development include parameter-tuning of the applications, to possibly achieve increased accuracy. Additionally, a non-rigid registration application should be developed, to account for local deformations during surgery. Development of additional tools for performing accurate validation of registration results should be developed as well.nb_NO
dc.languageengnb_NO
dc.publisherInstitutt for datateknikk og informasjonsvitenskapnb_NO
dc.subjectntnudaimno_NO
dc.subjectSIF2 datateknikkno_NO
dc.subjectIntelligente systemerno_NO
dc.titleMultimodal Volume to Volume Registration between Ultrasound and MRInb_NO
dc.typeMaster thesisnb_NO
dc.source.pagenumber135nb_NO
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for datateknikk og informasjonsvitenskapnb_NO


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