The Community for Technology Leaders
RSS Icon
Issue No.03 - May/June (2009 vol.15)
pp: 395-409
M. Alper Selver , Dokuz Eylül University, Izmir
Cüneyt Güzeliş , Dokuz Eylül University, Izmir
Being a tool that assigns optical parameters used in interactive visualization, Transfer Functions (TF) have important effects on the quality of volume rendered medical images. Unfortunately, finding accurate TFs is a tedious and time consuming task because of the trade off between using extensive search spaces and fulfilling the physician's expectations with interactive data exploration tools and interfaces. By addressing this problem, we introduce a semi-automatic method for initial generation of TFs. The proposed method uses a Self Generating Hierarchical Radial Basis Function Network to determine the lobes of a Volume Histogram Stack (VHS) which is introduced as a new domain by aligning the histograms of slices of a image series. The new self generating hierarchical design strategy allows the recognition of suppressed lobes corresponding to suppressed tissues and representation of the overlapping regions which are parts of the lobes but can not be represented by the Gaussian bases in VHS. Moreover, approximation with a minimum set of basis functions provides the possibility of selecting and adjusting suitable units to optimize the TF. Applications on different CT/MR data sets show enhanced rendering quality and reduced optimization time in abdominal studies.
Volume visualization, Transfer function design, medical image, Hierarchical radial basis function networks, multiscale analysis, Volume histogram stack
M. Alper Selver, Cüneyt Güzeliş, "Semiautomatic Transfer Function Initialization for Abdominal Visualization Using Self-Generating Hierarchical Radial Basis Function Networks", IEEE Transactions on Visualization & Computer Graphics, vol.15, no. 3, pp. 395-409, May/June 2009, doi:10.1109/TVCG.2008.198
[1] R.A. Drebin, L. Carpenter, and P. Hanrahan, “Volume Rendering,” Proc. ACM SIGGRAPH '88, pp. 65-74, 1988.
[2] C. Lundström, P. Ljung, and A. Ynnerman, “Local Histograms for Design of Transfer Functions in Direct Volume Rendering,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 6, pp.1570-1579, Nov./Dec. 2006.
[3] H. Pfister, B. Lorensen, C. Bajaj, G. Kindlmann, W. Schroeder, and R. Machiraju, “The Transfer Function Bake-Off,” Proc. 11th IEEE Visualization Conf. (VIS '00), pp. 523-526, 2000.
[4] C.L. Bajaj, V. Pascucci, and D.R. Schikore, “The Contour Spectrum,” Proc. Eighth IEEE Visualization Conf. (VIS '97), pp.167-173, 1997.
[5] G. Kindlmann and J.W. Durkin, “Semi-Automatic Generation of Transfer Functions for Direct Volume Rendering,” Proc. Ninth IEEE Visualization Conf. (VIS '98), pp. 79-86, 1998.
[6] I. Fujishiro, T. Azuma, and Y. Takeshima, “Automating Transfer Function Design for Comprehensible Volume Rendering Based on 3D Field Topology Analysis,” Proc. Ninth IEEE Visualization Conf. (VIS '98), pp. 467-470, 1998.
[7] T. He, L. Hong, A. Kaufman, and H. Pfister, “Generation of Transfer Functions with Stochastic Search Techniques,” Proc. Seventh IEEE Visualization Conf. (VIS '96), pp. 227-234, 1996.
[8] J. Kniss, G. Kindlmann, and C. Hansen, “Multi-Dimensional Transfer Functions for Interactive Volume Rendering,” IEEE Trans. Visualization and Computer Graphics, vol. 8, no. 3, pp.270-285, July-Sept. 2002.
[9] F. Shiaofen, B. Tom, and T. Mihran, “Image-Based Transfer Function Design for Data Exploration in Volume Visualization,” Proc. Ninth IEEE Visualization Conf. (VIS '98), pp. 319-326, 1998.
[10] J. Kniss, G. Kindlmann, and C. Hansen, “Interactive Volume Rendering Using Multi-Dimensional Transfer Functions and Direct Manipulation Widget,” Proc. 12th IEEE Visualization Conf. (VIS '01), pp. 255-262, 2001.
[11] A. Konig and E. Gröller, “Mastering Transfer Function Specification by Using VolumePro Technology,” Proc. 17th Spring Conf. Computer Graphics (SCCG '01), vol. 17, pp. 279-286, 2001.
[12] J. Marks, B. Andalman, P.A. Beardsley, and H. Pfister, “Design Galleries: A General Approach to Setting Parameters for Computer Graphics and Animation,” Proc. ACM SIGGRAPH '97, pp. 389-400, 1997.
[13] Y. Sato, C.F. Westin, A. Bhalerao, S. Nakajima, N. Shiraga, S. Tamura, and R. Kikinis, “Tissue Classification Based on 3DLocal Intensity Structures for Volume Rendering,” IEEE Trans. Visualization and Computer Graphics, vol. 6, no. 2, pp.160-180, Apr.-June 2000.
[14] E.B. Lum, J. Shearer, and K.-L. Ma, “Interactive Multi-Scale Exploration for Volume Classification,” The Visual Computer, vol. 22, no. 9-11, pp. 622-630, 2006.
[15] C. Lundström, A. Ynnerman, P. Ljung, A. Persson, and H. Knutsson, “The Alpha-Histogram: Using Spatial Coherence to Enhance Histograms and Transfer Function Design,” Proc. Eurographics/IEEE-VGTC Symp. Visualization (EuroVis), 2006.
[16] F.-Y. Tzeng, E.B. Lum, and K.-L. Ma, “An Intelligent System Approach to Higher-Dimensional Classification of Volume Data,” IEEE Trans. Visualization and Computer Graphics, vol. 11, no. 3, pp.273-284, May/June 2005.
[17] S. Roettger, M. Bauer, and M. Stamminger, “Spatialized Transfer Functions,” Proc. Eurographics/IEEE-VGTC Symp. Visualization (EuroVis '05), pp. 271-278, 2005.
[18] J.M. Kniss, R.L. Van Uitert Jr., A. Stephens, G.S. Li, T. Tasdizen, and C.D. Hansen, “Statistically Quantitative Volume Visualization,” Proc. 16th IEEE Visualization Conf. (VIS '05), pp. 287-294, 2005.
[19] P. Rautek, S. Bruckner, and M.E. Groller, “Semantic Layers for Illustrative Volume Rendering,” IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 6, pp. 1336-1343, Nov./Dec. 2007.
[20] C. Rezk Salama, M. Keller, and P. Kohlmann, “High-Level User Interfaces for Transfer Function Design with Semantics,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 5, pp. 1021-1028, Sept./Oct. 2006.
[21] Z. Fang, T. Möller, G. Hamarneh, and A. Celler, “Visualization and Exploration of Time-Varying Medical Image Data Sets,” Proc. Graphics Interface (GI '07), pp. 281-288, 2007.
[22] H. Akiba, N. Fout, and K.L. Ma, “Simultaneous Classification of Time-Varying Volume Data Based on the Time Histogram,” Proc. Eurographics/IEEE-VGTC Symp. Visualization (EuroVis '06), pp. 171-178, 2006.
[23] M.A. Selver, F. Fischer, M. Kuntalp, and W. Hillen, “A Software Tool for Interactive Generation, Representation, and Systematical Storage of Transfer Functions for 3D Medical Images,” Computer Methods and Programs in Biomedicine, vol. 86, pp. 270-280, 2007.
[24] D. Lowe, “Adaptive Radial Basis Function Nonlinearities, and the Problem of Generalization,” Proc. First IEE Conf. Artificial Neural Networks, pp. 171-175, 1989.
[25] S. Chen, C.F. Cowan, and P.M. Grant, “Orthogonal Least Squares Learning Algorithms for Radial Basis Function Networks,” IEEE Trans. Neural Networks, vol. 2, pp. 302-309, Mar. 1991.
[26] A. Sherstinsky and R.W. Picard, “On the Efficiency of the Orthogonal Least Squares Training Method for Radial Basis Function Networks,” IEEE Trans. Neural Networks, vol. 7, pp.195-200, Jan. 1996.
[27] T. Kohonen, Self-Organizing Maps. Springer-Verlag, 1995.
[28] R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis. Wiley, 1973.
[29] Z. Uykan, C. Güzeliş, M.E. Çelebi, and H.N. Koivo, “Analysis of Input-Output Clustering for Determining Centers of Radial Basis Function Networks,” IEEE Trans. Neural Networks, vol. 11, pp. 851-858, July 2000.
[30] K. Van Ha, “Hierarchical Radial Basis Function Networks,” Proc. Neural Network '98, pp. 1893-1898, 1998.
[31] P. Cerveri, C. Forlani, N. Borghese, and G. Ferrigno, “Distortion Correction for X-Ray Image Intensifiers: A Comparison between Local Un-Warping Polynomials and Adaptive Neural Networks,” Medical Physics, vol. 29, pp. 1759-1771, 2002.
[32] S. Ferrari, I. Frosio, V. Piuri, and N.A. Borghese, “Automatic Multiscale Meshing through HRBF Networks,” IEEE Trans. Instrumentation and Measurement, vol. 54, no. 4, pp. 1463-1470, Aug. 2005.
[33] S. Ferrari, M. Maggioni, and N.A. Borghese, “Multiscale Approximation with Hierarchical Radial Basis Function Networks,” IEEE Trans. Neural Networks, vol. 15, no. 14, pp. 178-188, 2004.
[34] J.A. Hartigan, Clustering Algorithms. Wiley, 1975.
[35] J. Park and I.W. Sandberg, “Universal Approximation Using Radial-Basis Function Networks,” Neural Computation, vol. 3, pp.257-546, 1991.
[36] M.T. Musavi, W. Ahmed, K.H. Chan, K.B. Faris, and D.M. Hummels, “On the Training of Radial Basis Function Classifiers,” Neural Networks, vol. 5, pp. 595-603, 1992.
[37] I. Rojas, H. Pomares, J. González, J.L. Bernier, E. Ros, F.J. Pelayo, and A. Prieto, “Analysis of the Functional Block Involved in the Design of Radial Basis Function Networks,” Neural Processing Letters, vol. 12, no. 1, pp. 1-17, Aug. 2000.
[38] S. Chen, S.A. Billings, and W. Luo, “Orthogonal Least Squares Methods and Their Application to Nonlinear System Identification,” Int'l J. Control, vol. 50, pp. 1873-1896, 1989.
[39] P.P. Kanjilal and D.N. Banerjee, “On the Application of Orthogonal Transformation for the Design and Analysis of Feedforward Networks,” IEEE Trans. Neural Networks, vol. 6, pp.1061-1070, 1995.
[40] S. Haykin, Neural Networks: A Comprehensive Foundation, second ed. Prentice Hall, 1999.
[41] OsirisX, http://www.osirix-viewer.comAboutOsiriX.html , Feb. 2008.
[42] M.A. Selver, A. Kocaoglu, G. Demir, H. Dogan, O. Dicle, and C. Guzelis, “Patient Oriented and Robust Automatic Liver Segmentation for Pre-Evaluation of Liver Transplantation,” Computers in Biology and Medicine, vol. 38, no. 7, pp. 765-784, 2008.
[43] H. Li and P. Santago, “Automatic Colon Segmentation with Dual Scan CT Colonography,” J. Digital Imaging, vol. 18, no. 1, pp. 42-54, 2005.
34 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool