Dimensionality reduction on multi-dimensional transfer functions for multi-channel volume data sets

Information Visualization

Volumn 9, Issue 3, 2010, Pages 167-180

  Kim, Han Suk ^a,b   Schulze, Jürgen P ^c   Cone, Angela C ^b   Sosinsky, Gina E ^b   Martone, Maryann E ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

dimensionality reduction; light microscopy imaging; multi channel volume; transfer function; volume rendering

Indexed keywords

COMPUTATIONAL APPROACH; DATA SETS; DESIGN PROCESS; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION ALGORITHMS; GRADIENT BASED; HIGH DIMENSIONAL DATA; LIGHT MICROSCOPY IMAGING; LOCALLY LINEAR EMBEDDING; MULTI-CHANNEL; MULTI-DIMENSIONAL TRANSFER FUNCTIONS; MULTICHANNEL DATA; MULTIPLE CHANNELS; MULTIPLE DATA; NON-TRIVIAL TASKS; NONLINEAR DIMENSIONALITY REDUCTION; PRINCIPLE COMPONENT ANALYSIS; TEXTURE PROPERTIES; THREE DIMENSIONS;

ALGORITHMS; CONFOCAL MICROSCOPY; DATA REDUCTION; DESIGN; VISUALIZATION; VOLUME RENDERING;

TRANSFER FUNCTIONS;

EID: 77956665564     PISSN: 14738716     EISSN: 14738724     Source Type: Journal    
DOI: 10.1057/ivs.2010.6     Document Type: Conference Paper

References (32)

1. Pfister, H., Lorensen, B., Schroeder, W., Bajaj, C. and Kindlmann, G. (2000) The transfer function bake-off. In: In: T. Ertl, B. Hamann and A. Varshney (eds.) VIS '00: Proceedings of the Conference on Visualization '00. Los Alamitos, CA: IEEE Computer Society Press, pp. 523-526.
2. Kindlmann, G. (2002) Transfer functions in direct volume rendering: Design, interface, interaction. In: T.S. Yoo with G. Gerig, R. Whitaker, R. Machiraju and T. Möller (eds.) Siggraph 2002 Course 50: Image Processing for Volume graphics.
3. Kim, H.S., Schulze, J.P., Cone, A.C., Sosinsky, G.E. and Martone, M.E. (2010) Multichannel transfer function with dimensionality reduction. Visualization and Data Analysis 2010 7530(1): 75300A, SPIE.
4. Kniss, J., Kindlmann, G. and Hansen, C. (2002) Multidimensional transfer functions for interactive volume rendering. IEEE Transactions on Visualization and Computer Graphics 8(3): 270-285.
5. Levoy, M. (1988) Display of surfaces from volume data. IEEE Computer Graphics and Applications 8(3): 29-37.
6. Kindlmann, G., Whitaker, R., Tasdizen, T. and Moller, T. (2003) Curvature-based transfer functions for direct volume rendering: methods and applications, Proccedings of the 14th IEEE Visualization 2003 (VIS'03), pp. 513-520.
7. Hladuvka, J., Konig, A. and Groller, E. (2000) Curvature-based transfer functions for direct volume rendering. In: B. Falcidieno (ed.) Spring Conference on Computer Graphics, pp. 58-65.
8. Correa, C. and Ma, K.-L. (2008) Size-based transfer functions: A new volume exploration technique. IEEE Transactions on Visualization and Computer Graphics 14(6): 1380-1387.
9. Caban, J. and Rheingans, P. (2008) Texture-based transfer functions for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics 14(6): 1364-1371.
10. Maciejewski, R., Woo, I., Chen, W. and Ebert, D. (2009) Structuring feature space: A non-parametric method for volumetric transfer function generation. IEEE Transactions on Visualization and Computer Graphics 15(6): 1473-1480.
11. Weinberger, K.Q. and Saul, L.K. (2006) An introduction to nonlinear dimensionality reduction by maximum variance unfolding. AAAI. Proceedings of the Twenty-first National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference; 16-20 July, Boston, MA: AAAI Press, http://dblp.uni.trier.de.
12. Weinberger, K.Q., Sha, F., Zhu, Q., Saul, L.K. (2007) Graph Laplacian regularization for large-scale semidefinite programming. In: B. Schoelkopf, J. Platt, and T. Hofmann (eds.) Advances in Neural Information Processing Systems 19, Cambridge, MA: MIT Press, pp. 1489-1496.
13. Roweis, S.T. and Saul, L.K. (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500): 2323-2326.
14. Belkin, M. and Niyogi, P. (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6): 1373-1396.
15. Donoho, D.L. and Grimes, C. (2003) Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. PNAS 100: 5591-5596.
16. Kohonen, T., Schroeder, M.R. and Huang, T.S. (eds.) (2000) Self- Organizing Maps. New York, New Jersy: Springer-Verlag; Secaucus.
17. MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. In: L.M. Le Cam and J. Neyman (eds.) Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol.1. Berkeley, CA: University of California Press, pp. 281-297.
18. Lewis, J.M., Hull, P.M., Weinberger, K.Q. and Saul, L.K. (2008) Mapping unchartered waters: Exploratory analysis, visualization, and clustering of oceanographic data. In: M.A. Wani, X. Chen, D. Casasent, L.A. Kurgan, T. Hu and K. Hafeez. ICMLA. Seventh International Conference on Machine Learning and Applications, 11-13 December, Sandiego, CA: IEEE Computer Society, http://dblp.uni.trier.de.
19. Tenenbaum, J.B., de Silva, V. and Langford, J.C. (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500): 2313-2319.
20. Silva, V.D. and Tenenbaum, J.B. (2003) Global versus local methods in nonlinear dimensionality reduction. Advances in Neural Information Processing Systems 15MIT Press, pp. 705-712.
21. Borg, I. and Groenen, P.J. (1997) Modern Multidimensional Scaling: Theory and Applications. Springer Series in Statistics. New York: Springer.
22. Duda, R.O., Hart, P.E. and Stork, D.G. (2000) Pattern Classification. 2nd edn. New York: Wiley-Interscience.
23. Dagum, L. and Menon, R. (1998) OpenMP: An industry standard API for shared-memory programming. Computational Science and Engineering, IEEE 5: 46-55.
24. Cormen, T.H., Leiserson, C.E., Rivest, R.L. and Stein, C. (2001) Introduction to Algorithms. Cambridge, MA: MIT Press and McGraw-Hill.
25. Kniss, J., Kindlmann, G. and Hansen, C. (2001) Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets. In: T. Ertl, K.I. Joy, and A. Varshney (eds.) VIS '01: Proceedings of the Conference on Visualization '01. Washington, DC, USA: IEEE Computer Society, pp. 255-262.
26. Friedrich, T. (2002) Nonlinear dimensionality reduction with locally linear embedding and isomap. MSc dissertation, Department of Computer Science, The University of Shefeld.
27. Martone, M.E. et al. (2002) A cell-centered database for electron tomographic data. Journal of Structural Biology 138: 145-155.
28. DeskVOX: VOlume eXplorer for the Desktop, http://www .calit2.net/ ∼jschulze/projects/vox.
29. Donoho, D.L. and Grimes, C. (2003) Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proccedings of the National Academy of Sciences of the United States of America-Biological Sciences. 100: 5591-5596. doi: 10.1073/pnas.1031596100.
30. He, T., Hong, L., Kaufman, A. and Pfister, H. (1996) Generation of transfer functions with stochastic search techniques. In: R. Yagel and G.M. Nielson (eds.) VIS '96: Proceedings of the 7th Conference on Visualization '96. Los Alamitos, CA: IEEE Computer Society Press, p. 227ff.
31. Tzeng, F.-Y., Lum, E.B. and Ma, K.-L. (2003) A novel interface for higher-dimensional classification of volume data. In: G. Turk, J.J. van Wijk and R.J. Moorhead II (eds.) VIS '03: Proceedings of the 14th IEEE Visualization 2003 (VIS'03). Washington, DC, USA: IEEE Computer Society, p. 66.
32. Pinto, F. and Freitas, C. (2008) Volume visualization and exploration through flexible transfer function design. Computers & Graphics 32: 420-429. N