Smart Super Views - A Knowledge-Assisted Interface for Medical Visualization

Gabriel Mistelbauer, Hamed Bouzari, Rüdiger Schernthaner, Ivan Baclija, Arnold Köchl, Stefan Bruckner, Milos Srámek, Meister Eduard Gröller
Smart Super Views - A Knowledge-Assisted Interface for Medical Visualization
In IEEE Conference on Visual Analytics Science and Technology (IEEE VAST) 2012, pages 163-172. October 2012.
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Abstract

Due to the ever growing volume of acquired data and information, users have to be constantly aware of the methods for their exploration and for interaction. Of these, not each might be applicable to the data at hand or might reveal the desired result. Owing to this, innovations may be used inappropriately and users may become skeptical. In this paper we propose a knowledge-assisted interface for medical visualization, which reduces the necessary effort to use new visualization methods, by providing only the most relevant ones in a smart way. Consequently, we are able to expand such a system with innovations without the users to worry about when, where, and especially how they may or should use them. We present an application of our system in the medical domain and give qualitative feedback from domain experts.

Additional Files and Images

Additional images and videos:
demo demo: Demo video (37 MB).
Additional files:
fastforward fastforward: Fast forward (4 MB).
paper paper: Full paper preprint.
rules rules: If-then rules for the fuzzy inference system.

BibTeX

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@inproceedings{mistelbauer-2012-ssv,
  title =      "Smart Super Views - A Knowledge-Assisted Interface for
               Medical Visualization",
  author =     "Gabriel Mistelbauer and Hamed Bouzari and R{\"u}diger
               Schernthaner and Ivan Baclija and Arnold K{\"o}chl and
               Stefan Bruckner and Milos Sr{\'a}mek and Meister Eduard
               Gr{\"o}ller",
  year =       "2012",
  abstract =   "Due to the ever growing volume of acquired data and
               information, users have to be constantly aware of the
               methods for their exploration and for interaction. Of these,
               not each might be applicable to the data at hand or might
               reveal the desired result. Owing to this, innovations may be
               used inappropriately and users may become skeptical. In this
               paper we propose a knowledge-assisted interface for medical
               visualization, which reduces the necessary effort to use new
               visualization methods, by providing only the most relevant
               ones in a smart way. Consequently, we are able to expand
               such a system with innovations without the users to worry
               about when, where, and especially how they may or should use
               them. We present an application of our system in the medical
               domain and give qualitative feedback from domain experts.",
  pages =      "163--172",
  month =      10,
  booktitle =  "IEEE Conference on Visual Analytics Science and Technology
               (IEEE VAST) 2012",
  publisher =  "IEEE Computer Society",
  location =   "Seattle, WA, USA",
  URL =        "http://www.cg.tuwien.ac.at/research/publications/2012/mistelbauer-2012-ssv/",
}