Abstract
This paper presents an adaptive method for visualization of tensor fields using multiresolution and viewer position and orientation. A particle tracing method is used in order to explore the benefits of motion to the human perceptual system. The particles are inserted and advected through the field based on a priority list which ranks tensors according to anisotropy measures and viewer parameters. Tensor fields representing colinear and coplanar structures are suitable for multiresolution analysis. Using multiple scales, we propose the use of anisotropic information in multiresolution, yielding an effective and simple method to compute priority values for particle creation. We also propose a new deterministic criterion for particle insertion in the field that balances their distribution in the tensor field domain. Our results show that our method enhances the visualization and reduces artifacts encountered in previous approaches.
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References
de Almeida Leonel, G., Peçanha, J.P., Vieira, M.B.: A Viewer-Dependent Tensor Field Visualization Using Particle Tracing. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part I. LNCS, vol. 6782, pp. 690–705. Springer, Heidelberg (2011)
Kondratieva, P., Krüger, J., Westermann, R.: The application of gpu particle tracing to diffusion tensor field visualization. In: Visualization, VIS 2005, pp. 73–78. IEEE (2005)
Shaw, C.D., Ebert, D.S., Kukla, J.M., Zwa, A., Soboroff, I., Roberts, D.A.: Data visualization using automatic, perceptually-motivated shapes. In: Proceeding of Visual Data Exploration and Analysis. SPIE (1998)
Shaw, C.D., Hall, J.A., Blahut, C., Ebert, D.S., Roberts, D.A.: Using shape to visualize multivariate data. In: NPIVM 1999: Proceedings of the 1999 Workshop on New Paradigms in Information Visualization and Manipulation in Conjunction with the Eighth ACM Internation Conference on Information and Knowledge Management, pp. 17–20. ACM, New York (1999)
Kindlmann, G.: Superquadric tensor glyphs. In: Proceedings of IEEE TVCG/EG Symposium on Visualization 2004, pp. 147–154 (May 2004)
Westin, C.F.: A Tensor Framework for Multidimensional Signal Processing. PhD thesis, Linköping University, Sweden, S-581 83 Linköping, Sweden (1994) Dissertation No. 348, ISBN 91-7871-421-4
Delmarcelle, T., Hesselink, L.: Visualization of second order tensor fields and matrix data. In: VIS 1992: Proceedings of the 3rd Conference on Visualization 1992, pp. 316–323. IEEE Computer Society Press, Los Alamitos (1992)
Delmarcelle, T., Hesselink, L.: Visualizing second-order tensor fields with hyper streamlines. IEEE Computer Graphics and Applications 13(4), 25–33 (1993)
Weinstein, D., Kindlmann, G., Lundberg, E.: Tensorlines: advection-diffusion based propagation through diffusion tensor fields. In: VIS 1999: Proceedings of the Conference on Visualization 1999, pp. 249–253. IEEE Computer Society Press, Los Alamitos (1999)
Vilanova, A., Zhang, S., Kindlmann, G., Laidlaw, D.: An introduction to visualization of diffusion tensor imaging and its applications. Visualization and Processing of Tensor Fields, 121–153 (2006)
McGraw, T., Nadar, M.: Stochastic dt-mri connectivity mapping on the gpu. IEEE Transactions on Visualization and Computer Graphics 13(6), 1504–1511 (2007)
Köhn, A., Klein, J., Weiler, F., Peitgen, H.: A gpu-based fiber tracking framework using geometry shaders. In: Proceedings of SPIE Medical Imaging, vol. 7261, p. 72611J (2009)
Evert, A., Neda, S., Andrei, J.: Cuda-accelerated geodesic ray-tracing for fiber tracking. International Journal of Biomedical Imaging (2011)
Mittmann, A., Nobrega, T., Comunello, E., Pinto, J., Dellani, P., Stoeter, P., von Wangenheim, A.: Performing real-time interactive fiber tracking. Journal of Digital Imaging 24(2), 339–351 (2011)
Crippa, A., Jalba, A., Roerdink, J.: Enhanced dti tracking with adaptive tensor interpolation. Visualization in Medicine and Life Sciences II, 175–192 (2012)
Rodrigues, P., Jalba, A., Fillard, P., Vilanova, A., ter Haar, B.: A multi-resolution watershed-based approach for the segmentation of diffusion tensor images. In: MICCAI Workshop on Diffusion Modelling, pp. 161–172 (2009)
Kindlmann, G.: Visualization and Analysis of Diffusion Tensor Fields. PhD thesis (September 2004)
Bahn, M.: Invariant and Orthonormal Scalar Measures Derived from Magnetic Resonance Diffusion Tensor Imaging. Journal of Magnetic Resonance 141(1), 68–77 (1999)
Delmarcelle, T., Hesselink, L.: Visualization of second order tensor fields and matrix data. In: Proceedings of IEEE Conference on Visualization 1992, pp. 316–323. IEEE (1992)
Mallat, S.: A Wavelet Tour of Signal Processing. The Sparse Way, 3rd edn. Academic Press (2008)
Kindlmann, G.: Diffusion tensor mri datasets, http://www.sci.utah.edu/~gk/DTI-data/
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de Souza Filho, J.L.R., Renhe, M.C., Vieira, M.B., de Almeida Leonel, G. (2012). A Viewer-dependent Tensor Field Visualization Using Multiresolution and Particle Tracing. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31075-1_53
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DOI: https://doi.org/10.1007/978-3-642-31075-1_53
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