Output-Sensitive Filtering of Streaming Volume Data

dc.contributor.authorSolteszova, Veronikaen_US
dc.contributor.authorBirkeland, Åsmunden_US
dc.contributor.authorStoppel, Sergejen_US
dc.contributor.authorViola, Ivanen_US
dc.contributor.authorBruckner, Stefanen_US
dc.contributor.editorChen, Min and Zhang, Hao (Richard)en_US
dc.date.accessioned2017-03-13T18:13:03Z
dc.date.available2017-03-13T18:13:03Z
dc.date.issued2017
dc.description.abstractReal‐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.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.12799
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.12799
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf12799
dc.publisher© 2017 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectvisibility
dc.subjectvolume data processing
dc.subjectobject-order imaging
dc.subjectCategories 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.titleOutput-Sensitive Filtering of Streaming Volume Dataen_US
Files
Collections