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dc.contributor.authorDíaz García, Jesús
dc.contributor.authorBrunet Crosa, Pere
dc.contributor.authorNavazo Álvaro, Isabel
dc.contributor.authorVázquez Alcocer, Pere Pau
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2017-12-01T09:24:50Z
dc.date.available2017-12-01T09:24:50Z
dc.date.issued2017
dc.identifier.citationDíaz, J., Brunet, P., Navazo, I., Vázquez, P. Downsampling methods for medical datasets. A: International Conference on Computer Graphics, Visualization, Computer Vision and image Processing. "Proceedings of the International conferences Computer Graphics, Visualization, Computer Vision and Image Processing 2017 and Big Data Analytics, Data Mining and Computational Intelligence 2017: Lisbon, Portugal, July 21-23, 2017". Lisbon: IADIS Press, 2017, p. 12-20.
dc.identifier.isbn978-989-8533-66-1
dc.identifier.urihttp://hdl.handle.net/2117/111411
dc.description.abstractVolume visualization software usually has to deal with datasets that are larger than the GPUs may hold. This is especially true in one of the most popular application scenarios: medical visualization. Typically, medical datasets are available for different personnel, but only radiologists have high-end systems that are able to cope with large data. For the rest of physicians, usually low-end systems are only available. As a result, most volume rendering packages downsample the data prior to uploading to the GPU. The most common approach consists in performing iterative subsampling along the longest axis, until the model fits inside the GPU memory. This causes important information loss that affects the final rendering. Some cleverer techniques may be developed to preserve the volumetric information. In this paper we explore the quality of different downsampling methods and present a new approach that produces smooth lower-resolution representations, yet still preserves small features that are prone to disappear with other approaches.
dc.format.extent9 p.
dc.language.isoeng
dc.publisherIADIS Press
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Infografia
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
dc.subject.lcshRendering (Computer graphics)
dc.subject.lcshThree dimensional imaging in medicine
dc.subject.otherMedical visualization
dc.subject.otherVolume rendering
dc.subject.otherDownsampling
dc.subject.otherGaussian filtering
dc.titleDownsampling methods for medical datasets
dc.typeConference report
dc.subject.lemacImatges tridimensionals en medicina
dc.contributor.groupUniversitat Politècnica de Catalunya. ViRVIG - Grup de Recerca en Visualització, Realitat Virtual i Interacció Gràfica
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.iadisportal.org/digital-library/downsampling-methods-for-medical-datasets
dc.rights.accessOpen Access
drac.iddocument21640319
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/PE 2013-2016/TIN2014-52211-C2-1-R
upcommons.citation.authorDíaz, J., Brunet, P., Navazo, I., Vázquez, P.
upcommons.citation.contributorInternational Conference on Computer Graphics, Visualization, Computer Vision and image Processing
upcommons.citation.pubplaceLisbon
upcommons.citation.publishedtrue
upcommons.citation.publicationNameProceedings of the International conferences Computer Graphics, Visualization, Computer Vision and Image Processing 2017 and Big Data Analytics, Data Mining and Computational Intelligence 2017: Lisbon, Portugal, July 21-23, 2017
upcommons.citation.startingPage12
upcommons.citation.endingPage20


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