Classifying Medical Projection Techniques based on Parameterization Attribute Preservation

dc.contributor.authorKreiser, Julianen_US
dc.contributor.authorRopinski, Timoen_US
dc.contributor.editorKai Lawonn and Mario Hlawitschka and Paul Rosenthalen_US
dc.date.accessioned2016-06-09T09:31:52Z
dc.date.available2016-06-09T09:31:52Z
dc.date.issued2016en_US
dc.description.abstractIn many areas of medicine, visualization researchers can help by contributing to to task simplification, abstraction or complexity reduction. As these approaches, can allow a better workflow in medical environments by exploiting easier communication through visualization, it is important to question their reliability and their reproducibility. Therefore, within this short paper, we investigate how projections used in medical visualization, can be classified with respect to the handled data and the underlying tasks. Many of these techniques are inspired by mesh parameterization, which allows for reducing a surface from R3 to R2. This makes complex structures often easier to understand by humans and machines. In the following section, we will classify different algorithms in this area (see Table 1) and discuss how these mappings benefit medical visualization.en_US
dc.description.sectionheadersClassificationen_US
dc.description.seriesinformationEuroVis Workshop on Reproducibility, Verification, and Validation in Visualization (EuroRV3)en_US
dc.identifier.doi10.2312/eurorv3.20161110en_US
dc.identifier.isbn978-3-03868-017-8en_US
dc.identifier.issn-en_US
dc.identifier.issn1017-4656en_US
dc.identifier.pages15-17en_US
dc.identifier.urihttps://doi.org/10.2312/eurorv3.20161110en_US
dc.identifier.urihttps://diglib.eg.org:443/handle/10
dc.publisherThe Eurographics Associationen_US
dc.subjecten_US
dc.titleClassifying Medical Projection Techniques based on Parameterization Attribute Preservationen_US
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