The Community for Technology Leaders
RSS Icon
Issue No.06 - November/December (2010 vol.16)
pp: 1301-1310
Stefan Lindholm , C-Research - Linköping University, Sweden and Siemens Corporate Research, USA
Patric Ljung , Siemens Corporate Research, USA
Claes Lundström , Sectra Imtec AB, Sweden
Anders Persson , CMIV - Link öping University - Linköping University Hospital, Sweden
Anders Ynnerman , C-Research - Linköping University, Sweden
In many applications of Direct Volume Rendering (DVR) the importance of a certain material or feature is highly dependent on its relative spatial location. For instance, in the medical diagnostic procedure, the patient's symptoms often lead to specification of features, tissues and organs of particular interest. One such example is pockets of gas which, if found inside the body at abnormal locations, are a crucial part of a diagnostic visualization. This paper presents an approach that enhances DVR transfer function design with spatial localization based on user specified material dependencies. Semantic expressions are used to define conditions based on relations between different materials, such as only render iodine uptake when close to liver. The underlying methods rely on estimations of material distributions which are acquired by weighing local neighborhoods of the data against approximations of material likelihood functions. This information is encoded and used to influence rendering according to the user's specifications. The result is improved focus on important features by allowing the user to suppress spatially less-important data. In line with requirements from actual clinical DVR practice, the methods do not require explicit material segmentation that would be impossible or prohibitively time-consuming to achieve in most real cases. The scheme scales well to higher dimensions which accounts for multi-dimensional transfer functions and multivariate data. Dual-Energy Computed Tomography, an important new modality in radiology, is used to demonstrate this scalability. In several examples we show significantly improved focus on clinically important aspects in the rendered images.
Direct Volume Rendering, Transfer Function, Spatial Conditioning, Neighborhood Meta-Data.
Stefan Lindholm, Patric Ljung, Claes Lundström, Anders Persson, Anders Ynnerman, "Spatial Conditioning of Transfer Functions Using Local Material Distributions", IEEE Transactions on Visualization & Computer Graphics, vol.16, no. 6, pp. 1301-1310, November/December 2010, doi:10.1109/TVCG.2010.195
[1] caBIG. The eXtensible Imaging Platform (XIP) project is an Open Source framework and platform for Medical Imaging. XIP is part of the caBIG initiative.. http:/
[2] M. Chen, D. Silver, A. S. Winter, V. Singh, and N. Cornea, Spatial transfer functions: a unified approach to specifying deformation in volume modeling and animation. In Proceedings of Eurographics/IEEE TVCG Workshop on Volume graphics, pages 35–44, 2003.
[3] C. Correa and K.-L. Ma, Size-based transfer functions: A new volume exploration technique. IEEE Transactions on Visualization and Computer Graphics, 14: 1380–1387, 2008.
[4] C. Correa and K.-L. Ma, The occlusion spectrum for volume classification and visualization. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1465–1472, 2009.
[5] R. A. Drebin, L. Carpenter, and P. Hanrahan, Volume rendering. Proceedings of Computer Graphics and Interactive Techniques, 22: 65–74, 1988.
[6] K. Engel, M. Hadwiger, J. M. Kniss, C. Rezk-Salama, and D. Weiskopf, Real-Time Volume Graphics. 1 edition, 2006. ISBN 1–56881–266–3.
[7] I. Fujishiro, T. Azuma, and Y. Takeshima, Automating transfer function design for comprehensible volume rendering based on 3d field topology. Proceedings of IEEE Visualization, pages –, 1999.
[8] M. Hadwiger, F. Laura, C. Rezk-Salama, T. Höllt, G. Geier, and T. Pabel, Interactive volume exploration for feature detection and quantification in industrial ct data. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1507–1514, 2008.
[9] M. Haidacher, D. Patel, S. Bruckner, A. Kanitsar, and M. E. Gröller, Volume visualization based on statistical transfer-function spaces. pages to appear-
[10] T. Johnson, B. Krau, M. Sedlmair, M. Grasruck, H. Bruder, D. Morhard, C. Fink, S. Weckbach, M. Lenhard, B. Schmidt, T. Flohr, M. Reiser, and C. Becker, Material differentiation by dual energy ct: initial experience. European Radiology, 17 (6), 2007.
[11] G. Kindlmann and J. Durkin, Semi-automatic generation of transfer functions for direct volume rendering. In IEEE Symposium on Volume Visualization, pages 79–86, 24–24 1998.
[12] J. Kniss, G. Kindlmann, and C. Hansen, Multidimensional transfer functions for interactive volume rendering. IEEE Transactions on Visualization and Computer Graphics, 8 (3): 270–285, 2002.
[13] J. Kniss, S. Premoze, M. Ikits, A. Lefohn, C. Hansen, and E. Praun, Gaussian transfer functions for multi-field volume visualization. page 65, 2003.
[14] D. H. Laidlaw, K. W. Fleischer, and A. H. Barr, Partial-volume bayesian classification of material mixtures in mr volume data using voxel histograms. IEEE Transactions on Medical Imaging, 17, 1998.
[15] C. Lundström, P. Ljung, and A. Ynnerman, Extending and simplifying transfer function design in medical volume rendering using local histograms. IEEE Computer Graphics Forum, pages 263–270, 2005.
[16] C. Lundström, P. Ljung, and A. Ynnerman, Local histograms for design of transfer functions in direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 12 (6): 1570–1579, 2006.
[17] C. Lundström, P. Ljung, and A. Ynnerman, Multi-dimensional transfer function design using sorted histograms. IEEE Workshop on Volume Graphics, pages 1–8,129, 2006.
[18] R. Maciejewski, I. Woo, W. Chen, and D. Ebert, Structuring feature space: A non-parametric method for volumetric transfer function generation. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1473–1480, nov.-dec. 2009.
[19] K. T. Nguyen, H. Ohlsson, A. Eklund, F. Hernell, P. Ljung, C. Forsell, M. Andersson, H. Knutsson, and A. Ynnerman, Concurrent volume visualization of real-time fmri. Proceedings of Eurographics/IEEE-VGTC Symposium on Volume Graphics, 2010.
[20] D. Patel, M. Haidacher, J.-P. Balabanian, and M. E. Groller, Moment curves. Proceedings of the IEEE Pacific Visualization Symposium, pages 201–208, 2009.
[21] A. Persson, C. Jackowski, E. Engstrm, and H. Zachrisson, Advances of dual source, dual-energy imaging in postmortem ct. European journal of radiology, 68 (3): 446–55, 2008.
[22] P. Rautek, Semantic Visualization Mapping for Volume Illustration. phD thesis, 2009.
[23] P. Rautek, S. Bruckner, and M. E. Gröller, Semantic layers for illustrative volume rendering. IEEE Transactions on Visualization and Computer Graphics, 13 (6): 1336–1343, 2007.
[24] P. Rautek, S. Bruckner, and M. E. Gröller, Interaction-dependent semantics for illustrative volume rendering. IEEE Computer Graphics Forum, 27 (3): 847–854, 2008.
[25] C. Rezk-Salama, M. Keller, and P. Kohlmann, High-level user interfaces for transfer function design with semantics. IEEE Transactions on Visualization and Computer Graphics, 11 (5): 1021–1028, 2006.
[26] C. Russ, C. Kubisch, F. Qiu, W. Hong, and P. Ljung, Real-time surface analysis and tagged material cleansing for virtual colonoscopy. IEEE Computer Graphics Forum, In Press, Corrected Proof:–, 2010.
[27] 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 Transactions on Visualization and Computer Graphics, 6 (2): 160–180, 2000.
[28] M. C. Sousa, D. S. Ebert, D. Stredney, and N. A. Svakhine, Illustrative visualization for medical training. Computational Aesthetics, pages 201–208, 2005.
[29] N. A. Svakhine, D. S. Ebert, and W. M. Andrews, Illustration-inspired depth enhanced volumetric medical visualization. IEEE Transactions on Visualization and Computer Graphics, 15 (1): 77–86, 2009.
[30] N. A. Svakhine, D. S. Ebert, and D. Stredney, Illustration motifs for effective medical volume illustration. IEEE Computer Graphics and Applications, 25 (3): 31–39, 2005.
[31] Y. Takeshima, S. Takahashi, I. Fujishiro, and G. Nielson, Introducing topological attributes for objective-based visualization. In Proceedings of IEEE Visualization, pages 6p–6p, 10–15 2004.
[32] S. Tenginakai, J. Lee, and R. Machiraju, Salient iso-surface detection with model-independent statistical signatures. Proceedings of IEEE Visualization.
[33] S. Tenginakai and R. Machiraju, Statistical computation of salient iso-values. Proceedings of the symposium on Data Visualisation, pages 19–24, 2002.
[34] F.-Y. Tzeng, E. B. Lum, and K.-L. Ma, A novel interface for higher-dimensional classification of volume data. Proceedings of IEEE Visualization, pages 505–512, 2003.
[35] F. Vega-Higuera and B. Krauss, Interactive tissue separation and visualization with dual-energy data on the gpu. Progress in biomedical optics and imaging, 9 (35), 2008.
[36] G. H. Weber, S. E. Dillard, H. Carr, V. Pascucci, and B. Hamann, Topology-controlled volume rendering. IEEE Transactions on Visualization and Computer Graphics, 13 (2): 330–341, 2007.
[37] M. Westenberg, J. Roerdink, and M. Wilkinson, Volumetric attribute filtering and interactive visualization using the max-tree representation. IEEE Transactions on Image Processing, 16 (12): 2943–2952, dec. 2007.
[38] H. Zachrisson, E. Engström, J. Engvall, L. Wigström, O. Smedby, and A. Persson, Soft tissue discrimination ex vivo by dual energy computed tomography. European Journal of Radiology, In Press, Corrected Proof:-, 2010.
[39] J. Zhou and M. Takatsuka, Automatic transfer function generation using contour tree controlled residue flow model and color harmonics. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1481–1488, 2009.
24 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool