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dc.contributor.authorBergmann, Ørjaneng
dc.contributor.authorLundervold, Arvideng
dc.contributor.authorSteihaug, Trondeng
dc.date.accessioned2008-10-16T12:22:21Z
dc.date.available2008-10-16T12:22:21Z
dc.date.issued2005eng
dc.identifier.citationComputer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on 2005: 277- 281en
dc.identifier.isbn0-7695-2355-2eng
dc.identifier.issn1063-7125eng
dc.identifier.urihttp://hdl.handle.net/1956/2799
dc.description.abstractDuring the last years, many techniques for de-noising, segmentation and fiber-tracking have been applied to diffusion tensor MR image data (DTI) from human and animal brains. However, evaluating such methods may be difficult on these data since there is no gold standard regarding the true geometry of the brain anatomy or fiber bundles reconstructed in each particular case. In order to study, validate and compare various de-noising and fiber-tracking methods, there is a need for a (mathematical) phantom consisting of semi-realistic images with well-known properties. In this work we generate such a phantom and provide a description of the calculation process all the way up to voxel-wise diffusion tensor visualization.en
dc.language.isoengeng
dc.publisherIEEEeng
dc.subjectBiomedical MRIeng
dc.subjectBraineng
dc.subjectImage denoisingeng
dc.subjectImage segmentationeng
dc.subjectMedical image processingeng
dc.subjectPhantomseng
dc.titleGenerating a synthetic diffusion tensor dataseteng
dc.typePeer reviewedeng
dc.typeJournal articleeng
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420nob
bora.peerreviewedPeer reviewedeng
bibo.doihttp://dx.doi.org/10.1109/CBMS.2005.58eng
dc.identifier.doihttp://dx.doi.org/10.1109/CBMS.2005.58


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