Kreiser, JulianRopinski, TimoKai Lawonn and Mario Hlawitschka and Paul Rosenthal2016-06-092016-06-092016978-3-03868-017-8-1017-4656https://doi.org/10.2312/eurorv3.20161110https://diglib.eg.org:443/handle/10In 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.10.2312/eurorv3.2016111015-17