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dc.contributor.advisorBlake, Richard E.nb_NO
dc.contributor.advisorLindseth, Franknb_NO
dc.contributor.advisorEidheim, Ole Christiannb_NO
dc.contributor.authorGjedrem, Stian Dalenenb_NO
dc.contributor.authorNavestad, Gunn Marienb_NO
dc.date.accessioned2014-12-19T13:32:58Z
dc.date.available2014-12-19T13:32:58Z
dc.date.created2010-09-03nb_NO
dc.date.issued2005nb_NO
dc.identifier348062nb_NO
dc.identifierntnudaim:1000nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/250920
dc.description.abstractWe have implemented and tested segmentation methods for segmenting brain tumours from magnetic resonance (MR) and ultrasound data. Our work in this thesis mainly focuses on active contours, both parametric (snakes) and geometric contours (level set). Active contours have the advantage over simpler segmentation methods that they are able to take both high- and low-level information into consideration. This means that the result they produce both depends on shape as well as intensity information from the input image. Our work is based on the results from an earlier completed depth study which investigated different segmentation methods. We have implemented and tested one simplified gradient vector flow snake model and four level set approaches: fast marching level set, geodesic level set, canny edge level set, and Laplacian level set. The methods are evaluated based on precision of the region boundary, sensitivity to noise, the effort needed to adjust parameters and the time to perform the segmentation. We have also compared the results with the result from a region growing method. We achieved promising results for active contour segmentation methods compared with other, simpler segmentation methods. The simplified snake model has given promising results, but has to be subject to more testing. Furthermore, tests with four variants of the level set method have given good results in most cases with MR data and in some cases with ultrasound data.nb_NO
dc.languageengnb_NO
dc.publisherInstitutt for datateknikk og informasjonsvitenskapnb_NO
dc.subjectntnudaimno_NO
dc.subjectSIF2 datateknikkno_NO
dc.subjectProgram- og informasjonssystemerno_NO
dc.titleSegmentation of Neuro Tumours: from MR and Ultrasound imagesnb_NO
dc.typeMaster thesisnb_NO
dc.source.pagenumber164nb_NO
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for datateknikk og informasjonsvitenskapnb_NO


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