Output-Sensitive Filtering of Streaming Volume Data

Veronika Solteszova, ├ůsmund Birkeland, Sergej Stoppel, Ivan Viola, Stefan Bruckner
Output-Sensitive Filtering of Streaming Volume Data
Computer Graphics Forum, 35(), 2016. [paper]

Information

Abstract

Real-time volume data acquisition poses substantial challenges for the traditional visualization pipeline where data enhancement is typically seen as a pre-processing step. In the case of 4D ultrasound data, for instance, costly processing operations to reduce noise and to remove artefacts need to be executed for every frame. To enable the use of high-quality filtering operations in such scenarios, we propose an output-sensitive approach to the visualization of streaming volume data. Our method evaluates the potential contribution of all voxels to the final image, allowing us to skip expensive processing operations that have little or no effect on the visualization. As filtering operations modify the data values which may affect the visibility, our main contribution is a fast scheme to predict their maximum effect on the final image. Our approach prioritizes filtering of voxels with high contribution to the final visualization based on a maximal permissible error per pixel. With zero permissible error, the optimized filtering will yield a result that is identical to filtering of the entire volume. We provide a thorough technical evaluation of the approach and demonstrate it on several typical scenarios that require on-the-fly processing.

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BibTeX

@article{Solteszova2016,
  title =      "Output-Sensitive Filtering of Streaming Volume Data",
  author =     "Veronika Solteszova and {Aa}smund Birkeland and Sergej
               Stoppel and Ivan Viola and Stefan Bruckner",
  year =       "2016",
  abstract =   "Real-time volume data acquisition poses substantial
               challenges for the traditional visualization pipeline where
               data enhancement is typically seen as a pre-processing step.
               In the case of 4D ultrasound data, for instance, costly
               processing operations to reduce noise and to remove
               artefacts need to be executed for every frame. To enable the
               use of high-quality filtering operations in such scenarios,
               we propose an output-sensitive approach to the visualization
               of streaming volume data. Our method evaluates the potential
               contribution of all voxels to the final image, allowing us
               to skip expensive processing operations that have little or
               no effect on the visualization. As filtering operations
               modify the data values which may affect the visibility, our
               main contribution is a fast scheme to predict their maximum
               effect on the final image. Our approach prioritizes
               filtering of voxels with high contribution to the final
               visualization based on a maximal permissible error per
               pixel. With zero permissible error, the optimized filtering
               will yield a result that is identical to filtering of the
               entire volume. We provide a thorough technical evaluation of
               the approach and demonstrate it on several typical scenarios
               that require on-the-fly processing.",
  journal =    "Computer Graphics Forum",
  volume =     "35",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2016/Solteszova2016/",
}