Output-Sensitive Filtering of Streaming Volume Data

Veronika Solteszova, Åsmund Birkeland, Sergej Stoppel, Ivan Viola, Stefan Bruckner

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)249-262
Number of pages14
JournalComputer Graphics Forum
Volume36
Issue number1
DOIs
StatePublished - Jan 1 2017

Keywords

  • Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics] Three-Dimensional Graphics and Realism – Visible line/surface algorithms. I.4.3 [Image Processing and Computer Vision] Enhancement – Filtering
  • object-order imaging
  • visibility
  • volume data processing

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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