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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kniss, J.M. Van Uitert, R. Stephens, A. Li, G.-S. Tasdizen, T. Hansen, C. |
| Copyright Year | 2005 |
| Description | Author affiliation: Utah Univ., Salt Lake City, UT, USA (Kniss, J.M.) |
| Abstract | Visualization users are increasingly in need of techniques for assessing quantitative uncertainty and error in the images produced. Statistical segmentation algorithms compute these quantitative results, yet volume rendering tools typically produce only qualitative imagery via transfer function-based classification. This paper presents a visualization technique that allows users to interactively explore the uncertainty, risk, and probabilistic decision of surface boundaries. Our approach makes it possible to directly visualize the combined "fuzzy" classification results from multiple segmentations by combining these data into a unified probabilistic data space. We represent this unified space, the combination of scalar volumes from numerous segmentations, using a novel graph-based dimensionality reduction scheme. The scheme both dramatically reduces the dataset size and is suitable for efficient, high quality, quantitative visualization. Lastly, we show that the statistical risk arising from overlapping segmentations is a robust measure for visualizing features and assigning optical properties. |
| Starting Page | 287 |
| Ending Page | 294 |
| File Size | 1765239 |
| Page Count | 8 |
| File Format | |
| ISBN | 0780394623 |
| DOI | 10.1109/VISUAL.2005.1532807 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-10-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Data visualization Image segmentation Transfer functions Robustness Rendering (computer graphics) Risk analysis Biological materials Measurement uncertainty Magnetic resonance imaging Humans |
| Content Type | Text |
| Resource Type | Article |
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