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A Novel Image-Centric Approach Toward Direct Volume Rendering

Published: 30 January 2018 Publication History

Abstract

Transfer function (TF) generation is a fundamental problem in direct volume rendering (DVR). A TF maps voxels to color and opacity values to reveal inner structures. Existing TF tools are complex and unintuitive for the users who are more likely to be medical professionals than computer scientists. In this article, we propose a novel image-centric method for TF generation where instead of complex tools, the user directly manipulates volume data to generate DVR. The user’s work is further simplified by presenting only the most informative volume slices for selection. Based on the selected parts, the voxels are classified using our novel sparse nonparametric support vector machine classifier, which combines both local and near-global distributional information of the training data. The voxel classes are mapped to aesthetically pleasing and distinguishable color and opacity values using harmonic colors. Experimental results on several benchmark datasets and a detailed user survey show the effectiveness of the proposed method.

References

[1]
G. Camps-Valls and L. Bruzzone. 2005. Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 43, 6, 1351--1362.
[2]
D. Cohen-Or, O. Sorkine, R. Gal, T. Leyvand, and Y.-Q Xu. 2006. Color harmonization. ACM Transactions on Graphics 25, 3, 624--630.
[3]
C. D. Correa and K. L Ma. 2008. Size-based transfer functions: A new volume exploration technique. IEEE Transactions on Visualization and Computer Graphics 14, 6, 1380--1387.
[4]
M. Cristianini and J. Shawe-Taylor. 2000. An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, UK.
[5]
S. Fang, T. Biddlecome, and M. Tuceryan. 1998. Image-based transfer function design for data exploration in volume visualization. In Proceedings of the Conference on Visualization (VIS’98). IEEE, Los Alamitos, CA, 319--326.
[6]
Joachim Giesen, Klaus Mueller, Eva Schuberth, Lujin Wang, and Peter Zolliker. 2007. Conjoint analysis to measure the perceived quality in volume rendering. IEEE Transactions on Visualization and Computer Graphics 13, 6, 1664--1671.
[7]
H. Guo, W. Li, and X. Yuan. 2014. Transfer function map. In Proceedings of the IEEE Pacific Visualization Symposium. IEEE, Los Alamitos, CA, 262--266.
[8]
H. Guo, N. Mao, and X. Yuan. 2011a. WYSIWYG (what you see is what you get) volume visualization. IEEE Transactions on Visualization and Computer Graphics 17, 12, 2106--2114.
[9]
H. Guo, H. Xiao, and X. Yuan. 2011b. Multi-dimensional transfer function design based on flexible dimension projection embedded in parallel coordinates. In Proceedings of the IEEE Pacific Visualization Symposium. IEEE, Los Alamitos, CA, 19--26.
[10]
H. Guo and X. Yuan. 2013. Local WYSIWYG volume visualization. In Proceedings of the IEEE Pacific Visualization Symposium. IEEE, Los Alamitos, CA, 65--72.
[11]
M. Hadwiger, L. Fritz, C. Rezk-Salama, T. Hollt, G. Geier, and T. Pabel. 2008. Interactive volume exploration for feature detection and quantification in industrial CT data. IEEE Transactions on Visualization and Computer Graphics 14, 6, 1507--1514.
[12]
M. Hadwiger, J. M. Kniss, C. Rezk-Salama, D. Weiskopf, and K. Engel. 2006. Real-Time Volume Graphics. A. K. Peters, Ltd., Natick, MA.
[13]
J. Itten. 1969. The Art of Color: The Subjective Experience and Objective Rationale of Color. Van Nostrand Reinhold Company.
[14]
N. M. Khan, R. Ksantini, I. S. Ahmad, and L. Guan. 2014a. SN-SVM: A sparse nonparametric support vector machine classifier. Signal, Image and Video Processing 8, 8, 1625--1637.
[15]
N. M. Khan, M. Kyan, and L. Guan. 2014b. ImmerVol: An immersive volume visualization system. In Proceedings of the IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA’14). IEEE, Los Alamitos, CA, 24--29.
[16]
N. M. Khan, M. Kyan, and L. Guan. 2015. Intuitive volume exploration through spherical self-organizing map and color harmonization. Neurocomputing 147, 160--173.
[17]
G. Kindlmann and J. W. Durkin. 1998. Semi-automatic generation of transfer functions for direct volume rendering. In Proceedings of the 1998 IEEE Symposium on Volume Visualization. ACM, New York, NY, 79--86.
[18]
Kitware™. 2015. VolView. (2015). Retrieved December 29, 2017, from http://www.kitware.com/volview/.
[19]
J. Kniss, G. Kindlmann, and C. Hansen. 2001. Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets. In Proceedings of the IEEE Symposium on Volume Visualization. IEEE, Los Alamitos, CA, 255--262.
[20]
M. Levoy. 1988. Display of surfaces from volume data. IEEE Computer Graphics and Applications 8, 3, 29--37.
[21]
R. Likert. 1932. A technique for the measurement of attitudes. Archives of Psychology 22, 140, 1--55.
[22]
B. Liu, B. Wnsche, and T. Ropinski. 2010. Visualization by example—a constructive visual component-based interface for direct volume rendering. In Proceedings of the International Conference on Computer Graphics Theory and Applications. 254--259.
[23]
Y. Liu, C. Lisle, and J. Collins. 2011. Quick2Insight: A user-friendly framework for interactive rendering of biological image volumes. In Proceedings of the IEEE Symposium on Biological Data Visualization. IEEE, Los Alamitos, CA, 1--8.
[24]
C. Lundstrom, P. Ljung, and A. Ynnerman. 2006a. Local histograms for design of transfer functions in direct volume rendering. IEEE Transactions on Visualization and Computer Graphics 12, 6, 1570--1579.
[25]
C. Lundstrom, A. Ynnerman, P. Ljung, A. Persson, and H. Knutsson. 2006b. The -histogram: Using spatial coherence to enhance histograms and transfer function design. In Proceedings of the IEEE Symposium on Visualization. IEEE, Los Alamitos, CA, 227--234.
[26]
R. Maciejewski, Y. Jang, I. Woo, H. Janicke, K. P. Gaither, and D. S. Ebert. 2013. Abstracting attribute space for transfer function exploration and design. IEEE Transactions on Visualization and Computer Graphics 18, 1, 94--107.
[27]
R. Maciejewski, I. Wu, W. Chen, and D. Ebert. 2009. Structuring feature space: A non-parametric method for volumetric transfer function generation. IEEE Transactions on Visualization and Computer Graphics 15, 6, 1473--1480.
[28]
J. Marks, B. Andalman, P. A. Beardsley, W. Freeman, S. Gibson, J. Hodgins, T. Kang, et al. 1997. Design galleries: A general approach to setting parameters for computer graphics and animation. In Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques. ACM, New York, NY, 389--400.
[29]
S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and K. R. Mullers. 1999. Fisher discriminant analysis with kernels. Neural Networks for Signal Processing IX: Proceedings of the IEEE Signal Processing Society Workshop. 41--48.
[30]
Binh Nguyen, Wei-Liang Tay, Chee-Kong Chui, and Sim-Heng Ong. 2012. A clustering-based system to automate transfer function design for medical image visualization. Visual Computer 28, 2, 181--191.
[31]
H. Pfister, B. Lorensen, C. Bajaj, G. Kindlmann, W. Schroeder, L. S. Avila, K. M. Raghu, R. Machiraju, and J. Lee. 2001. The transfer function bake-off. IEEE Computer Graphics and Applications 21, 3, 16--22.
[32]
F. M. Pinto and C. M. D. S. Freitas. 2007. Design of multi-dimensional transfer functions using dimensional reduction. In Proceedings of the Eurographics Symposium on Visualization. IEEE, Los Alamitos CA, 131--138.
[33]
S. Roettger, M. Bauer, and M. Stamminger. 2005. Spatialized transfer functions. In Proceedings of the IEEE/Eurographics Symposium on Visualization. IEEE, Los Alamitos, CA, 271--278.
[34]
W. Schroeder, K. Martin, and B. Lorensen (Eds.). 2006. The Visualization Toolkit. Kitware, New York, NY.
[35]
M. Selver, M. Alper, and C. Guzeli. 2009. Semiautomatic transfer function initialization for abdominal visualization using self-generating hierarchical radial basis function networks. IEEE Transactions on Visualization and Computer Graphics 15, 3, 395--409.
[36]
P. Sereda, A. V. Bartroli, I. W. O. Serlie, and F. A. Gerritsen. 2006a. Visualization of boundaries in volumetric data sets using LH histograms. IEEE Transactions on Visualization and Computer Graphics 12, 2, 208--218.
[37]
P. Sereda, A. Vilanova, and F. A. Gerritsen. 2006b. Automating transfer function design for volume rendering using hierarchical clustering of material boundaries. In Proceedings of the IEEE Symposium on Visualization. IEEE, Los Alamitos, CA, 243--250.
[38]
C. Studholme, D. L. G. Hill, and D. J. Hawkes. 1999. An overlap invariant entropy measure for 3D medical image alignment. Pattern Recognition 32, 71--86.
[39]
A. Tappenbeck, B. Preim, and V. Dicken. 2006. Distance-based transfer function design: Specification methods and applications. In Proceedings of the Conference on Simulation and Visualization (SimVis’06). IEEE, Los Alamitos, CA, 259--274.
[40]
M. Tokumaru, N. Muranaka, and S. Imanishi. 2002. Color design support system considering color harmony. In Proceedings of the IEEE International Conference on Fuzzy Systems. IEEE, Los Alamitos, CA, 378--383.
[41]
F.-Y. Tzeng. 2006. Intelligent System-Assisted User Interfaces for Volume Visualization. Ph.D. Dissertation. University of California.
[42]
Vivek Walimbe, Vladimir Zagrodsky, Shanker Raja, Wael A. Jaber, Frank P. DiFilippo, Mario J. Garcia, Richard C. Brunken, James D. Thomas, and Raj Shekhar. 2003. Mutual information-based multimodality registration of cardiac ultrasound and SPECT images: A preliminary investigation. International Journal of Cardiovascular Imaging 19, 483--494.
[43]
Yunhai Wang, Wei Chen, Jian Zhang, Tingxing Dong, Guihua Shan, and Xuebin Chi. 2011. Efficient volume exploration using the Gaussian mixture model. IEEE Transactions on Visualization and Computer Graphics 17, 11, 1560--1573.
[44]
S. Wesarg, M. Kirschner, and M. F. Khan. 2010. 2D histogram based volume visualization: Combining intensity and size of anatomical structures. IEEE Transactions on Visualization and Computer Graphics 5, 6, 655--666.
[45]
Yingcai Wu, Haumin Qu, Ka-Kei Chung, and Ming-Yuen Chan. 2007. Quantitative effectiveness measures for direct volume rendered images. In Proceedings of the IEEE Pacific Visualization Symposium. IEEE, Los Alamitos, CA, 1--8.
[46]
F. Zhou, Y. Zhao, and K. Ma. 2010. Parallel mean shift for interactive volume segmentation. In Proceedings of the International Conference on Machine Learning in Medical Imaging. IEEE, Los Alamitos, CA, 67--75.
[47]
J. Zhou and M. Takatsuka. 2009. 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.

Cited By

View all
  • (2022)A real-time image-centric transfer function design based on incremental classificationJournal of Real-Time Image Processing10.1007/s11554-021-01176-x19:1(185-203)Online publication date: 1-Feb-2022
  • (2021)GPU-based multi-slice per pass algorithm in interactive volume illumination rendering交互式体积光照绘制中基于GPU的单绘制遍多切片算法Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.200021422:8(1092-1103)Online publication date: 28-Aug-2021
  • (2019)CNNs Based Viewpoint Estimation for Volume VisualizationACM Transactions on Intelligent Systems and Technology10.1145/330999310:3(1-22)Online publication date: 12-Apr-2019

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Published In

cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 9, Issue 4
Research Survey and Regular Papers
July 2018
280 pages
ISSN:2157-6904
EISSN:2157-6912
DOI:10.1145/3183892
  • Editor:
  • Yu Zheng
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 30 January 2018
Accepted: 01 October 2017
Revised: 01 May 2017
Received: 01 October 2016
Published in TIST Volume 9, Issue 4

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Author Tags

  1. Volume visualization
  2. intelligent systems
  3. medical imaging
  4. pattern recognition
  5. support vector machine

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View all
  • (2022)A real-time image-centric transfer function design based on incremental classificationJournal of Real-Time Image Processing10.1007/s11554-021-01176-x19:1(185-203)Online publication date: 1-Feb-2022
  • (2021)GPU-based multi-slice per pass algorithm in interactive volume illumination rendering交互式体积光照绘制中基于GPU的单绘制遍多切片算法Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.200021422:8(1092-1103)Online publication date: 28-Aug-2021
  • (2019)CNNs Based Viewpoint Estimation for Volume VisualizationACM Transactions on Intelligent Systems and Technology10.1145/330999310:3(1-22)Online publication date: 12-Apr-2019

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