This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
An Intelligent System Approach to Higher-Dimensional Classification of Volume Data
May/June 2005 (vol. 11 no. 3)
pp. 273-284
In volume data visualization, the classification step is used to determine voxel visibility and is usually carried out through the interactive editing of a transfer function that defines a mapping between voxel value and color/opacity. This approach is limited by the difficulties in working effectively in the transfer function space beyond two dimensions. We present a new approach to the volume classification problem which couples machine learning and a painting metaphor to allow more sophisticated classification in an intuitive manner. The user works in the volume data space by directly painting on sample slices of the volume and the painted voxels are used in an iterative training process. The trained system can then classify the entire volume. Both classification and rendering can be hardware accelerated, providing immediate visual feedback as painting progresses. Such an intelligent system approach enables the user to perform classification in a much higher dimensional space without explicitly specifying the mapping for every dimension used. Furthermore, the trained system for one data set may be reused to classify other data sets with similar characteristics.

[1] C.L. Bajaj, V. Pascucci, and D.R. Shikore, “The Contour Spectrum,” Proc. IEEE Visualization '97 Conf., pp. 167-175, 1997.
[2] V. Blanz, B. Schölkopf, H. Bülthoff, C. Burges, V. Vapnik, and T. Vetter, “Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models,” Proc. Int'l Conf. Artificial Neural Networks, pp. 251-256, 1996.
[3] B.E. Boser, I. Guyon, and V. Vapnik, “A Training Algorithm for Optimal Margin Classifiers,” Proc. Fifth Ann. Workshop Computational Learning Theory, pp. 144-152, 1992.
[4] K.J. Cherkauer and J.W. Shavlik, “Rapid Quality Estimation of Neural Network Input Representations,” Advances in Neural Information Processing Systems, vol. 8, pp. 45-51, MIT Press, 1996.
[5] C. Cortes and V. Vapnik, “Support Vector Network,” Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[6] N. Friedman and D. Geiger, and M. Goldszmidt, “Bayesian Network Classifiers,” Machine Learning, vol. 29, nos. 2-3, pp. 131-163, 1997.
[7] I. Fujishiro, T. Azuma, and Y. Takeshima, “Automating Transfer Function Design for Comprehensible Volume Rendering Based on 3D Field Topology Analysis,” Proc. IEEE Visualization '99 Conf., pp. 467-470, 1999.
[8] A. VanGelder and U. Hoffman, “Direct Volume Rendering with Shading via Three-Dimensional Textures,” Proc. ACM Symp. Volume Visualization '96 Conf. Proc., pp. 23-30, 1996.
[9] E. Gelenbe, Y. Feng, K. Ranga, and R. Krishnan, “Neural Networks for Volumetric MR Imaging of the Brain,” Proc. Int'l Workshop Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, pp. 194-202, 1996.
[10] L.O. Hall, A.M. Bensaid, L.P. Clarke, R.P. Velthuizen, M.S. Silbiger, and J.C. Bezdek, “A Comparison of Neural Network and Fuzzy Clustering Techniques in Segmenting Magnetic Resonance Images of the Brain,” IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 672-682, 1992.
[11] T. He, L. Hong, A. Kaufman, and H. Pfister, “Generation of Transfer Functions with Stochastic Search Techniques,” Proc. IEEE Visualization '96 Conf., pp. 227-234, 1996.
[12] M.A. Hearst, “Trends and Controversies: Support Vector Machines,” IEEE Intelligent Systems, vol. 13, no. 4, pp. 18-28, 1998.
[13] R. Huang and K.-L. Ma, “RGVis: Region Growing Based Techniques for Volume Visualization,” Proc. Pacific Graphics '03 Conf., pp. 355-363, 2003.
[14] T.J. Jankun-Kelly and K.-L. Ma, “A Study of Transfer Function Generation for Time-Varying Volume Data,” Proc. Joint IEEE TCVG and Eurographics Workshop, pp. 51-68, 2001.
[15] G. Kindlmann and J.W. Durkin, “Semi-Automatic Generation of Transfer Functions for Direct Volume Rendering,” Proc. '98 IEEE Symp. Volume Visualization, pp. 79-86, 1998.
[16] J. Kniss, G. Kindlmann, and C. Hansen, “Interactive Volume Rendering Using Multi-Dimensional Transfer Functions and Direct Manipulation Widgets,” Proc. IEEE Visualization '01 Conf., pp. 255-262, 2001.
[17] A. König and E. Gröller, “Mastering Transfer Function Specification by Using VolumePro Technology,” Proc. Spring Conf. Computer Graphics, vol. 17, pp. 279-286, 2001.
[18] M. Levoy, “Display of Surfaces from Volume Data,” IEEE Computer Graphics and Applications, vol. 8, no. 3, pp. 29-37, 1988.
[19] J. Marks, B. Andalman, P. Beardsley, W. Freeman, S. Gibson, J. Hodgins, T. Kang, B. Mirtich, H. Pfister, W. Ruml, K. Ryall, J. Seims, and S. Shieber, “Design Galleries: A General Approach to Setting Parameters for Computer Graphics and Animation,” Proc. SIGGRAPH '97, pp. 389-400, 1997.
[20] A. McCallum, D. Freitag, and F. Pereira, “Maximum Entropy Markov Models for Information Extraction and Segmentation,” Proc. 17th Int'l Conf. Machine Learning, pp. 591-598, 2000.
[21] E. Osuna, R. Freund, and F. Girosi, “Training Support Vector Machines: An Application to Face Detection,” Proc. '97 Conf. Computer Vision and Pattern Recognition, pp. 130-137, 1997.
[22] L.I. Perlovsky, Neural Networks and Intellect: Using Model-Based Concepts. Oxford Univ. Press, 2000.
[23] 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 Computer Graphics and Applications, vol. 21, no. 3, pp. 16-22, May/June 2001.
[24] D. Rumelhart and J. McClelland, Parallel and Distributed Processing: Explorations in the Microstructure of Cognition. The MIT Press, 1986.
[25] I. Takanashi, E.B. Lum, K.-L. Ma, and S. Muraki, “ISpace: Interactive Volume Data Classification Techniques Using Independent Component Analysis,” Proc. Pacific Graphics '02 Conf., pp. 366-374, 2002.
[26] J. Thorsten, “Text Categorization with Support Vector Machines: Learning with Many Relevant Features,” Proc. 10th European Conf. Machine Learning, pp. 137-142, 1998.
[27] F.-Y. Tzeng, E.B. Lum, and K.-L. Ma, “A Novel Interface for Higher-Dimensional Classification of Volume Data,” Proc. IEEE Visualization '03 Conf., pp. 505-512, 2003.
[28] P. Werbos, “Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences,” PhD thesis, Dept. of Applied Math., Harvard Univ., 1974.

Index Terms:
User interface design, classification, transfer functions, graphics hardware, visualization, volume rendering, machine learning.
Citation:
Fan-Yin Tzeng, Eric B. Lum, Kwan-Liu Ma, "An Intelligent System Approach to Higher-Dimensional Classification of Volume Data," IEEE Transactions on Visualization and Computer Graphics, vol. 11, no. 3, pp. 273-284, May-June 2005, doi:10.1109/TVCG.2005.38
Usage of this product signifies your acceptance of the Terms of Use.