Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/17513
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DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | BEKAERT, Philippe | - |
dc.contributor.author | KOVAC, Thomas | - |
dc.date.accessioned | 2014-10-09T09:14:04Z | - |
dc.date.available | 2014-10-09T09:14:04Z | - |
dc.date.issued | 2014 | - |
dc.identifier.uri | http://hdl.handle.net/1942/17513 | - |
dc.description.abstract | In 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.mimetype | Application/pdf | - |
dc.language | nl | - |
dc.language.iso | en | - |
dc.publisher | tUL | - |
dc.title | Computational science: Medical imaging using CUDA | - |
dc.type | Theses and Dissertations | - |
local.bibliographicCitation.jcat | T2 | - |
dc.description.notes | master in de informatica-multimedia | - |
local.type.specified | Master thesis | - |
item.accessRights | Open Access | - |
item.fullcitation | KOVAC, Thomas (2014) Computational science: Medical imaging using CUDA. | - |
item.contributor | KOVAC, Thomas | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | Master theses |
Files in This Item:
File | Description | Size | Format | |
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09282352013196.pdf | 2.09 MB | Adobe PDF | View/Open |
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