Output-Sensitive Filtering of Streaming Volume Data

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dc.contributor.author Solteszova, Veronika en_US
dc.contributor.author Birkeland, Åsmund en_US
dc.contributor.author Stoppel, Sergej en_US
dc.contributor.author Viola, Ivan en_US
dc.contributor.author Bruckner, Stefan en_US
dc.contributor.editor Chen, Min and Zhang, Hao (Richard) en_US
dc.date.accessioned 2017-03-13T18:13:03Z
dc.date.available 2017-03-13T18:13:03Z
dc.date.issued 2017
dc.identifier.issn 1467-8659
dc.identifier.uri http://dx.doi.org/10.1111/cgf.12799
dc.identifier.uri https://diglib.eg.org:443/handle/10.1111/cgf12799
dc.description.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.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 outputsensitive 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. en_US
dc.publisher © 2017 The Eurographics Association and John Wiley & Sons Ltd. en_US
dc.subject visibility
dc.subject volume data processing
dc.subject object-order imaging
dc.subject 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
dc.title Output-Sensitive Filtering of Streaming Volume Data en_US
dc.description.seriesinformation Computer Graphics Forum
dc.description.sectionheaders Articles
dc.description.volume 36
dc.description.number 1
dc.identifier.doi 10.1111/cgf.12799


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