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Issue No.12 - Dec. (2011 vol.17)
pp: 1969-1978
Martin Haidacher , Institute of Computer Graphics and Algorithms, Vienna University of Technology
Eduard Gröller , Institute of Computer Graphics and Algorithms, Vienna University of Technology
ABSTRACT
The combination of volume data acquired by multiple modalities has been recognized as an important but challenging task. Modalities often differ in the structures they can delineate and their joint information can be used to extend the classification space. However, they frequently exhibit differing types of artifacts which makes the process of exploiting the additional information non-trivial. In this paper, we present a framework based on an information-theoretic measure of isosurface similarity between different modalities to overcome these problems. The resulting similarity space provides a concise overview of the differences between the two modalities, and also serves as the basis for an improved selection of features. Multimodal classification is expressed in terms of similarities and dissimilarities between the isosurfaces of individual modalities, instead of data value combinations. We demonstrate that our approach can be used to robustly extract features in applications such as dual energy computed tomography of parts in industrial manufacturing.
INDEX TERMS
Multimodal data, volume visualization, surface similarity.
CITATION
Martin Haidacher, Eduard Gröller, "Volume Analysis Using Multimodal Surface Similarity", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 12, pp. 1969-1978, Dec. 2011, doi:10.1109/TVCG.2011.258
REFERENCES
[1] H. Akiba and K.-L. Ma, A tri-space visualization interface for analyzing time-varying multivariate volume data. In Proceedings of EuroVis 2007, pages 115–122, 2007.
[2] J. F. Barrett and K. Nicholas, Artifacts in CT: Recognition and avoidance. Radiographics, 24 (6): 1679–1691, 2004.
[3] S. Bruckner and T. Möller, Isosurface similarity maps. Computer Graphics Forum, 29(3): 773–782, 2010.
[4] W. Cai and G. Sakas, Data intermixing and multi-volume rendering. In Computer Graphics Forum, volume 18, pages 359–368, 1999.
[5] H. Carr, B. Duffy, and B. Denby, On histograms and isosurface statistics. IEEE Transactions on Visualization and Computer Graphics, 12 (5): 1259–1265, 2006.
[6] H. Carr, J. Snoeyink, and M. van de Panne, Flexible isosurfaces: Simplifying and displaying scalar topology using the contour tree. Computational Geometry: Theory and Applications, 43 (1): 42–58, 2010.
[7] M. Chen and H. Jänicke, An information-theoretic framework for visualization. IEEE Transactions on Visualization and Computer Graphics, 16 (6): 1206–1215, 2010.
[8] M. Chen and J. V. Tucker, Constructive volume geometry. Computer Graphics Forum, 19: 281–293, 2000.
[9] C. Eusemann, D. R. Holmes III, B. Schmidt, T. G. Flohr, R. Robb, C. Mc-Collough, D. M. Hough, J. E. Huprich, M. Wittmer, H. Siddiki, and J. G. Fletcher, Dual energy CT: How to best blend both energies in one fused image? In Proceedings of SPIE Medical Imaging 2008, pages 1–8, 2008.
[10] A. Evans, S. Marrett, J. Torrescorzo, S. Ku, and L. Collins, MRI-PET correlation in three dimensions using a volume-of-interest (VOI) atlas. Journal of Cerebral Blood Flow and Metabolism, 11 (2): A69–A78, 1991.
[11] S. Fang and R. Srinivasan, Volumetric-CSG - a model-based volume visualization approach. In Proceedings of the 6th International Conference in Central Europe on Computer Graphics and Visualization, pages 88– 95, 1998.
[12] M. Feixas, M. Sbert, and F. González, A unified information-theoretic framework for viewpoint selection and mesh saliency. ACM Transactions on Graphics, 6: 1–23, 2009.
[13] M. Ferre, A. Puig, and D. Tost, A framework for fusion methods and rendering techniques of multimodal volume data: Research articles. Computer Animation and Virtual Worlds, 15: 63–77, 2004.
[14] R. Fuchs and H. Hauser, Visualization of multi-variate scientific data. Computer Graphics Forum, 28 (6): 1670–1690, 2009.
[15] M. Haidacher, S. Bruckner, A. Kanitsar, and M. E. Gröller, Information-based transfer functions for multimodal visualization. In Proceedings of Visual Computing for Biomedicine 2008, pages 101–108, 2008.
[16] C. Heinzl, J. Kastner, and M. E. Gröller, Surface extraction from multi-material components for metrology using dual energy CT. IEEE Transactions on Visualization and Computer Graphics, 13 (6): 1520–1527, 2007.
[17] H. Hong, J. Bae, H. Kye, and Y.-G. Shin, Efficient multimodality volume fusion using graphics hardware. In Proceedings of the International Conference on Computational Science 2005, pages 842–845, 2005.
[18] J. Hsieh, Computed Tomography: Principles, Design, Artifacts and Recent Advances. SPIE Press, 2003.
[19] X. Huang, N. Paragios, and D. Metaxas, Shape registration in implicit spaces using information theory and free form deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28: 1303–1318, 2006.
[20] M. Jones, J. Baerentzen, and M. Srámek, 3D distance fields: a survey of techniques and applications. IEEE Transactions on Visualization and Computer Graphics, 12 (4): 581–599, 2006.
[21] M. Khoury and R. Wenger, On the fractal dimension of isosurfaces. IEEE Transactions on Visualization and Computer Graphics, 16 (6): 1198– 1205, 2010.
[22] J. Kim, S. Eberl, and D. Feng, Visualizing dual-modality rendered volumes using a dual-lookup table transfer function. Computing in Science and Engineering, 9 (1): 20–25, 2007.
[23] G. Kindlmann and J. W. Durkin, Semi-automatic generation of transfer functions for direct volume rendering. In Proceedings of the IEEE Symposium on Volume Visualization 1998, pages 79–86, 1998.
[24] J. Kniss, C. Hansen, M. Grenier, and T. Robinson, Volume rendering multivariate data to visualize meteorological simulations: a case study. In Proceedings of VisSym 2002, pages 189–195, 2002.
[25] J. Kniss, J. P. Schulze, U. Wössner, P. Winkler, U. Lang, and C. Hansen, Medical applications of multi-field volume rendering and VR techniques. In Proceedings of VisSym 2004, pages 249–254, 2004.
[26] T. Kvålseth, Entropy and correlation: Some comments. IEEE Transactions on Systems, Man, and Cybernetics, 17: 517–519, 1987.
[27] D. Levin, X. Hu, K. Tan, S. Galhotra, C. Pelizzari, G. Chen, R. Beck, C. Chen, M. Cooper, and J. Mullan, The brain: integrated three-dimensional display of MR and PET images. Radiology, 172: 783–789, 1989.
[28] S. Lloyd, Least squares quantization in PCM. IEEE Transactions on Information Theory, 28 (2): 129–137, 1982.
[29] M. E. Noz, G. Q. Maguire, M. P. Zeleznik, E. L. Kramer, F. Mahmoud, and J. Crafoord, A versatile functional/anatomic image fusion method for volume data sets. Journal of Medical Systems, 25 (5): 297–307, 2001.
[30] C. Scheidegger, J. Schreiner, B. Duffy, H. Carr, and C. Silva, Revisiting histograms and isosurface statistics. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1659–1666, 2008.
[31] C. E. Shannon, A mathematical theory of communication. Bell System Technical Journal, 27: 379–423,623–656, 1948.
[32] S. Takahashi, Y. Takeshima, I. Fujishiro, and G. Nielson, Emphasizing isosurface embeddings in direct volume rendering. In G. Bonneau, T. Ertl, and G. Nielson editors, Scientific Visualization: The Visual Extraction of Knowledge from Data, pages 185–206. Springer, 2006.
[33] I. Viola, M. Feixas, M. Sbert, and M. E. Gröller, Importance-driven focus of attention. IEEE Transactions on Visualization and Computer Graphics, 12: 933–940, 2006.
[34] P. Šereda, A. V. Bartrolí, I. W. Serlie, and F. A. Gerritsen, Visualization of boundaries in volumetric data sets using LH histograms. IEEE Transactions on Visualization and Computer Graphics, 12 (2): 208–218, 2006.
[35] C. Wang and H.-W. Shen, Information theory in scientific visualization. Entropy, 13 (1): 254–273, 2011.
[36] S. W. Wang and A. E. Kaufman, Volume-sampled 3D modeling. IEEE Computer Graphics and Applications, 14 (5): 26–32, 1994.
[37] Z. Wang, D. Ziou, C. Armenakis, D. Li, and Q. Li, A comparative analysis of image fusion methods. IEEE Transactions on Geoscience and Remote Sensing, 43: 1391–1402, 2005.
[38] W. Wells III, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, Multi-modal volume registration by maximization of mutual information. Medical image analysis, 1 (1): 35–51, 1996.
[39] L. Xu, T.-Y. Y, and H.-W. Shen, An information-theoretic framework for flow visualization. IEEE Transactions on Visualization and Computer Graphics, 16 (6): 1216–1224, 2010.
[40] Y. Yao, Information-theoretic measures for knowledge discovery and data mining. In Karmeshu, editor, Entropy Measures, Maximum Entropy Principle and Emerging Application, pages 115–136. Springer, 2003.
[41] L. Zadeh, Fuzzy sets. Information and Control, 8: 338–353, 1995.
[42] K. J. Zuiderveld and M. A. Viergever, Multi-modal volume visualization using object-oriented methods. In Proceedings of the IEEE Symposium on Volume Visualization 1994, pages 59–66, 1994.
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