Visibility-Driven Processing of Streaming Volume Data

Information

• Publication Type: Conference Paper
• Workgroup(s)/Project(s):
  • Visual Computing: Illustrative Visualization
• Date: September 2014
• ISBN: 978-3-905674-62-0
• Publisher: Eurographics Association
• Location: Vienna, Austria
• Lecturer: Veronika Solteszova
• ISSN: 2070-5778
• Event: 4th EG Workshop on Visual Computing and Biology Medicine
• Editor: Ivan Viola and Katja Buehler and Timo Ropinski
• Booktitle: Proceedings of EG VCBM 2014
• Conference date: 4. September 2014 – 5. September 2014
• Pages: 127 – 136

Abstract

In real-time volume data acquisition, such as 4D ultrasound, the raw data is challenging to visualize directly without additional processing. Noise removal and feature detection are common operations, but many methods are too costly to compute over the whole volume when dealing with live streamed data. In this paper, we propose a visibility-driven processing scheme for handling costly on-the-fly processing of volumetric data in real-time. In contrast to the traditional visualization pipeline, our scheme utilizes a fast computation of the potentially visible subset of voxels which significantly reduces the amount of data required to process. As filtering operations modify the data values which may affect their visibility, our method for visibility-mask generation ensures that the set of elements deemed visible does not change after processing. Our approach also exploits the visibility information for the storage of intermediate values when multiple operations are performed in sequence, and can therefore significantly reduce the memory overhead of longer filter pipelines. We provide a thorough technical evaluation of the approach and demonstrate it on several typical scenarios where on-the-fly processing is required.

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BibTeX

@inproceedings{Viola_Ivan_VDP,
  title =      "Visibility-Driven Processing of Streaming Volume Data",
  author =     "Veronika Solteszova and {\AA}smund Birkeland and Ivan Viola
               and Stefan Bruckner",
  year =       "2014",
  abstract =   "In real-time volume data acquisition, such as 4D ultrasound,
               the raw data is challenging to visualize directly without
               additional processing. Noise removal and feature detection
               are common operations, but many methods are too costly to
               compute over the whole volume when dealing with live
               streamed data. In this paper, we propose a visibility-driven
               processing scheme for handling costly on-the-fly processing
               of volumetric data in real-time. In contrast to the
               traditional visualization pipeline, our scheme utilizes a
               fast computation of the potentially visible subset of voxels
               which significantly reduces the amount of data required to
               process. As filtering operations modify the data values
               which may affect their visibility, our method for
               visibility-mask generation ensures that the set of elements
               deemed visible does not change after processing. Our
               approach also exploits the visibility information for the
               storage of intermediate values when multiple operations are
               performed in sequence, and can therefore significantly
               reduce the memory overhead of longer filter pipelines. We
               provide a thorough technical evaluation of the approach and
               demonstrate it on several typical scenarios where on-the-fly
               processing is required.",
  month =      sep,
  isbn =       "978-3-905674-62-0",
  publisher =  "Eurographics Association",
  location =   "Vienna, Austria",
  issn =       "2070-5778",
  event =      "4th EG Workshop on Visual Computing and Biology Medicine",
  editor =     "Ivan Viola and Katja Buehler and Timo Ropinski",
  booktitle =  "Proceedings of EG VCBM 2014",
  pages =      "127--136",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2014/Viola_Ivan_VDP/",
}