Joe M. Kniss and Robert Van Uitert and Abraham Stephens and Guo-Shi Li and Tolga Tasdizen and Charles Hansen.
"Statistically Quantitative Volume Visualization".
In Proceedings of IEEE Visualization 2005, pp. 287--294, 2005.

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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 classi.cation. 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 uni.ed probabilistic data space. We represent this uni.ed 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 eficient, 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.

Summary:

Keywords:

Volume visualization, uncertainty, classi.cation, risk analysis

Bibtex:

@InProceedings{  kniss:2005:SQVV,
  author = 	 "Joe M. Kniss and Robert Van Uitert and Abraham
                  Stephens and Guo-Shi Li and Tolga Tasdizen and
                  Charles Hansen",
  title = 	 "Statistically Quantitative Volume Visualization",
  booktitle = 	 "Proceedings of IEEE Visualization 2005",
  pages = 	 "287--294",
  year =         "2005",
}

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