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.
Images References 

Links: 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 functionbased 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” classi.cation 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 e.cient, 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:
Categories:
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",
}
Images:
References:
    References
  1. C. Bajaj, V. Pascucci, and D. Schikore. The Contour Spectrum. In IEEE Visualization 1997, pages 167-173, 1997.
  2. K. S. Bonnell, M. A. Duchaineau, D. Schikore, B. Hamann, and K. I. Joy. Material interface reconstruction. IEEE Transactions on Visualization and Computer Graphics(TVCG), 9(4):500-511, 2003.
  3. C. Cocosco, V. Kollokian, R. Kwan, and A. Evens. Brainweb: Online interface to a 3d mri brain database. In NeuroImage, http://www.bic.mni.mcgill.ca/brainweb/, 1997.
  4. R. A. Drebin, L. Carpenter, and P. Hanrahan. Volume rendering. Computer Graphics (Proceedings of SIGGRAPH '88), 22(4):65-74, August 1988.
  5. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classifi- cation. Wiley, 2000.
  6. K. Engel, M. Kraus, and T. Ertl. High-Quality Pre- Integrated Volume Rendering Using Hardware-Accelerated Pixel Shading. In Eurographics / SIGGRAPH Workshop on Graphics Hardware '01, Annual Conference Series, pages 9-16. Addison-Wesley Publishing Company, Inc., 2001.
  7. L. Grady. Multilabel random walker image segmentation using prior models. In CVPR 2005, 2005 (Accepted).
  8. G. Grigoryan and P. Rheingans. Point-based probabilistic surfaces to show surface uncertainty. TVCG, 5(10):564-573, 2004.
  9. M. Hadwiger, C. Berger, and H. Hauser. High-quality twolevel volume rendering of segmented data sets on consumer graphics hardware. In IEEE Visualization 2003, 2003.
  10. L. Ibanez, W. Schroeder, L. Ng, and J. Cates. The ITK Software Guide: The Insight Segmentation and Registration Toolkit. Kitware, Inc, 2003.
  11. T. Iwata, K. Saito, N. Ueda, S. Stromsten, T. L. Gri.ths, and J. B. Tenenbaum. Parametric embedding for class visualization. In Advances in Neural Information Processing Systems 17, pages 617-624. 2005.
  12. C. Johnson. Top scienti.c visualization research problems. IEEE Computer Graphics and Applications, pages 13-17, 2004.
  13. A. Kaufman and K. Mueller. The Visualization Handbook, chapter Overview of Volume Rendering. Elsevier, 2004.
  14. G. Kindlmann. Semi-automatic generation of transfer functions for direct volume rendering. Masters thesis, Cornell University, 1999.
  15. J. Kniss, G. Kindlmann, and C. Hansen. Multidimensional transfer functions for interactive volume rendering. IEEE TVCG, 8(3):270-285, jul - sep 2002.
  16. D. Laidlaw. Geometric Model Extraction from Magnetic Resonance Volume Data. PhD thesis, California Institute of Technology, 1995.
  17. M. Levoy. A hybrid ray tracer for rendering polygon and volume data. IEEE Computer Graphics & Applications, 10(2):33-40, March 1990.
  18. S. Roweis and L. Saul. Nonlinear dimensionality reduction by local linear embedding. Science, 290:2323-2326, Dec 2000.
  19. L. Sobierajski and A. Kaufman. Volumetric ray tracing. In Symposium on Volume Visualization, pages 11-18, October 1994.
  20. D. Stalling, M. Zoeckler, and H.C. Hege. Segmentation of 3d medical images with subvoxel accuracy. In Computer Assisted Radiology and Surgery, pages 137-142, 1998.
  21. T. Tasdizen, S. Awate, R. Whitaker, and N. Foster. Mri tissue classi.cation with neighborhood statistics: A nonparametric, entropy-minimizing approach. In MICCAI 2005, 2005 (Accepted).
  22. J. Tenenbaum, V. de Silva, and J. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319-2323, Dec 2000.
  23. U. Tiede, T. Schiemann, and K. H. Hohne. High quality rendering of attributed volume data. In IEEE Visualization 1999, pages 255-262, 1999.
  24. F. Tzeng, E. Lum, and K. Ma. An intelligent system approach to higher dimensional classification of volume data. IEEE TVCG, 11(3):273-284, may - jun 2005.
  25. F. Tzeng and K. Ma. A cluster-space visual interface for arbitrary dimensional classification of volume data. In IEEE TCVG Symposium on Visualization, 2004.
  26. I. Viola, A. Kanitsar, and M. E. Gr¨oller. Importance-driven volume rendering. In Proceedings of IEEE Visualization '04, pages 139-145, 2004.
  27. C. M. Wittenbrink, A. Pang, and S. Lodha. Glyphs for visualizing uncertainty in vector .elds. IEEE TVCG, 2(3):266-279, September 1996.