Analysis of biomedical data with multilevel glyphs

By Heimo Müller, Robert Reihs, Kurt Zatloukal, Andreas Holzinger. 2014

42 citations recorded by the source corpus.

This paper presents multilevel data glyphs optimized for the interactive knowledge discovery and visualization of large biomedical data sets. Data glyphs are three- dimensional objects defined by multiple levels of geometric descriptions (levels of detail) combined with a mapping of data attributes to graphical elements and methods, which specify their spatial position. In the data mapping phase, which is done by a biomedical expert, meta information about the data attributes (scale, number of distinct values) are compared with the visual capabilities of the graphical elements in order to give a feedback to the user about the correctness of the variable mapping. The spatial arrangement of glyphs is done in a dimetric view, which leads to high data density, a simplified 3D navigation and avoids perspective distortion. We show the usage of data glyphs in the disease analyser a visual analytics application for personalized medicine and provide an outlook to a biomedical web visualization scenario. Data glyphs can be successfully applied in the disease analyser for the analysis of big medical data sets. Especially the automatic validation of the data mapping, selection of subgroups within histograms and the visual comparison of the value distributions were seen by experts as an important functionality.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Molecular Biology

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Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, methodology): “To overview and compare collections from diﬀerent catalogues, visual analytics techniques are necessary, especially glyph based visualization techniques (M¨uller et al., 2014).”
State-of-the-Art and Future Challenges in the Integration of Biobank Catalogues (2015, background): “[2] also show the usage of data glyphs in a visual analytics application and provide an outlook to a biomedical web visualization scenario, the combination of focus and context principle and different level of details are shown in Fig.”
Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research (2016, methodology): “A good example for interactive knowledge discovery is the work by Mueller et al. [9] where. in the data-mapping phase, which is done by a biomedical expert, the data attributes of the meta-information are compared with the visual capabilities of the graphical elements in order to give a feedback to…”