Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/17513
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dc.contributor.advisorBEKAERT, Philippe-
dc.contributor.authorKOVAC, Thomas-
dc.date.accessioned2014-10-09T09:14:04Z-
dc.date.available2014-10-09T09:14:04Z-
dc.date.issued2014-
dc.identifier.urihttp://hdl.handle.net/1942/17513-
dc.description.abstractIn deze thesis wordt een framework voorgesteld die kan worden gebruikt bij longitudinale studies van MS progressie. De theoretische en praktische aspecten van dit framework worden uitvoerig besproken alsook grondig geëvalueerd. Het geïmplementeerde framework werd ook geoptimaliseerd gebruikmakend van CUDA.-
dc.format.mimetypeApplication/pdf-
dc.languagenl-
dc.language.isoen-
dc.publishertUL-
dc.titleComputational science: Medical imaging using CUDA-
dc.typeTheses and Dissertations-
local.bibliographicCitation.jcatT2-
dc.description.notesmaster in de informatica-multimedia-
local.type.specifiedMaster thesis-
item.fulltextWith Fulltext-
item.contributorKOVAC, Thomas-
item.fullcitationKOVAC, Thomas (2014) Computational science: Medical imaging using CUDA.-
item.accessRightsOpen Access-
Appears in Collections:Master theses
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