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Issue No.06 - November/December (2009 vol.15)
pp: 1465-1472
Carlos Correa , University of California at Davis
Kwan-Liu Ma , University of California at Davis
Despite the ever-growing improvements on graphics processing units and computational power, classifying 3D volume data remains a challenge.In this paper, we present a new method for classifying volume data based on the ambient occlusion of voxels. This information stems from the observation that most volumes of a certain type, e.g., CT, MRI or flow simulation, contain occlusion patterns that reveal the spatial structure of their materials or features. Furthermore, these patterns appear to emerge consistently for different data sets of the same type. We call this collection of patterns the \emph{occlusion spectrum} of a dataset. We show that using this occlusion spectrum leads to better two-dimensional transfer functions that can help classify complex data sets in terms of the spatial relationships among features. In general, the ambient occlusion of a voxel can be interpreted as a weighted average of the intensities in a spherical neighborhood around the voxel. Different weighting schemes determine the ability to separate structures of interest in the occlusion spectrum. We present a general methodology for finding such a weighting. We show results of our approach in 3D imaging for different applications, including brain and breast tumor detection and the visualization of turbulent flow.
Transfer Functions, Ambient Occlusion, Volume Rendering, Interactive Classification
Carlos Correa, Kwan-Liu Ma, "The Occlusion Spectrum for Volume Classification and Visualization", IEEE Transactions on Visualization & Computer Graphics, vol.15, no. 6, pp. 1465-1472, November/December 2009, doi:10.1109/TVCG.2009.189
[1] C. L. Bajaj, V. Pascucci, and D. R. Schikore, The contour spectrum. In Proc. IEEE Visualization '97, pages 167–173, Los Alamitos, CA, USA, 1997. IEEE Computer Society Press.
[2] M. Bunnell, GPU Gems 2, chapter Dynamic Ambient Occlusion and Indirect Lighting, pages 223–233. Addison Wesley, 2005.
[3] C. Correa and K.-L. Ma, Size-based transfer functions: A new volume exploration technique. IEEE Trans. on Visualization and Computer Graphics, 14 (6): 1380–1387, 2008.
[4] I. Fujishiro, T. Azuma, and Y. Takeshima, Automating transfer function design for comprehensible volume rendering based on 3d field topology analysis. In IEEE Visualization, pages 467–470, 1999.
[5] Y. Gong, Y. Zhang, W. Chen, and Q. Peng, Dynamic anisotropic occlusion. In Eurographcs 2006 Short Presentations, 2006.
[6] F. González, M. Sbert, and M. Feixas, An information-theoretic ambient occlusion. In Computational Aesthetics, pages 29–36, 2007.
[7] M. Hadwiger, P. Ljung, C. R. Salama, and T. Ropinski, Advanced illumination techniques for GPU volume raycasting. In SIGGRAPH Asia '08: ACM SIGGRAPH ASIA 2008 courses, pages 1–166, 2008.
[8] J. Hladůvka, A. König, and E. Gröller, Curvature-based transfer functions for direct volume rendering. In Spring Conference on Computer Graphics 2000 (SCCG 2000), volume 16, pages 58–65, 2000.
[9] R. Huang and K.-L. Ma, Rgvis: Region growing based techniques for volume visualization. In Proc. Pacific Conference on Computer Graphics and Applications, pages 355–363, 2003.
[10] N. C. Institute, 2009.
[11] G. Kindlmann and J. W. Durkin, Semi-automatic generation of transfer functions for direct volume rendering. In VVS '98: Proceedings of the 1998 IEEE symposium on Volume visualization, pages 79–86, 1998.
[12] G. Kindlmann, R. Whitaker, T. Tasdizen, and T. Moller, Curvature-based transfer functions for direct volume rendering: Methods and applications. In Proc. IEEE Visualization 2003, pages 513–520, 2003.
[13] M. Knecht, State of the art report on ambient occlusion. techreport, Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstrasse 9-11/186, A-1040 Vienna, Austria, 2007.
[14] J. Kniss, G. Kindlmann, and C. Hansen, Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets. In Proc. IEEE Visualization 2001, pages 255–262, 2001.
[15] J. Kontkanen and S. Laine, Ambient occlusion fields. In Proc. Symposium on Interactive 3D graphics and games, pages 41–48, 2005.
[16] M. Levoy, Display of surfaces from volume data. IEEE Comput. Graph. Appl., 8 (3): 29–37, 1988.
[17] E. B. Lum and K.-L. Ma, Lighting transfer functions using gradient aligned sampling. In Proc. IEEE Visualization '04, pages 289–296, 2004.
[18] C. Lundstrom, P. Ljung, and A. Ynnerman, Local histograms for design of transfer functions in direct volume rendering. IEEE Trans. on Visualization and Computer Graphics, 12 (6): 1570–1579, 2006.
[19] A. Méndez-Feliu and M. Sbert, From obscurances to ambient occlusion: A survey. Visual Computer, 25 (2): 181–196, 2009.
[20] H. Pfister, B. Lorensen, C. Bajaj, G. Kindlmann, W. Schroeder, L. S. Avila, K. Martin, R. Machiraju, and J. Lee, The transfer function bake-off. IEEE Comput. Graph. Appl., 21 (3): 16–22, 2001.
[21] C. Rezk-Salama, M. Keller, and P. Kohlmann, High-level user interfaces for transfer function design with semantics. IEEE Trans. on Visualization and Computer Graphics, 12 (5): 1021–1028, 2006.
[22] T. Ritschel, Fast GPU-based Visibility Computation for Natural Illumination of Volume Data Sets. In P. Cignoni, and J. Sochor editors, Short Paper Proc. of Eurographics 2007, pages 17–20, 2007.
[23] S. Roettger, M. Bauer, and M. Stamminger, Spatialized transfer functions. In EuroVis, pages 271–278, 2005.
[24] T. Ropinski, J. Meyer-Spradow, S. Diepenbrock, J. Mensmann, and K. Hinrichs, Interactive volume rendering with dynamic ambient occlusion and color bleeding. Comp. Graphics Forum, 27 (2): 567–576, 2008.
[25] M. Ruiz, I. Boada, I. Viola, S. Bruckner, M. Feixas, and M. Sbert, Obscurance-based volume rendering framework. In Proc. of Volume Graphics 2008, pages 113–120, 2008.
[26] Y. Sato, C.-F. Westin, A. Bhalerao, S. Nakajima, N. Shiraga, S. Tamura, and R. Kikinis, Tissue classification based on 3d local intensity structure for volume rendering. IEEE Trans on Visualization and Computer Graphics, 6 (2): 160–180, 2000.
[27] M. Sattler, R. Sarlette, G. Zachmann, and R. Klein, Hardware-accelerated ambient occlusion computation. In B. Girod, M. Magnor, and H.-P. Seidel editors, , Vision, Modeling, and Visualization 2004, pages 331–338. Akademische Verlagsgesellschaft Aka GmbH, Berlin, 2004.
[28] P. Sereda, A. Vilanova, I. W. O. Serlie, and F. A. Gerritsen, Visualization of boundaries in volumetric data sets using LH histograms. IEEE Trans. on Visualization and Computer Graphics, 12 (2): 208–218, 2006.
[29] S. Takahashi, Y. Takeshima, and I. Fujishiro, Topological volume skeletonization and its application to transfer function design. Graph. Models, 66 (1): 24–49, 2004.
[30] S. Takahashi, Y. Takeshima, I. Fujishiro, and G. M. Nielson, Emphasizing isosurface embeddings in direct volume rendering. Scientific Visualization: The Visual Extraction of Knowledge from Data, pages 185–206,255.
[31] M. Tarini, P. Cignoni, and C. Montani, Ambient occlusion and edge cueing for enhancing real time molecular visualization. IEEE Trans. on Visualization and Computer Graphics, 12 (5): 1237–1244, 2006.
[32] F.-Y. Tzeng, E. B. Lum, and K.-L. Ma, A novel interface for higher-dimensional classification of volume data. In Proc. IEEE Visualization 2003, pages 505–512, 2003.
[33] C. Wyman, S. Parker, P. Shirley, and C. Hansen, Interactive display of isosurfaces with global illumination. IEEE Trans. on Visualization and Computer Graphics, 12 (2): 186–196, 2006.
[34] X. Zhang and C. Bajaj, Extraction, quantification and visualization of protein pockets. Computational Systems Bioinformatics Conference, 6: 275–286, 2007.
[35] S. Zhukov, A. Inoes, and G. Kronin, An ambient light illumination model. In G. Drettakis, and N. Max editors, Rendering Techniques '98, Euro-graphics, pages 45–56. Springer-Verlag Wien New York, 1998.
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