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The Visual Computer

, Volume 32, Issue 6–8, pp 835–845 | Cite as

Adaptive transfer functions

Improved multiresolution visualization of medical models
  • Jesús Díaz-García
  • Pere Brunet
  • Isabel Navazo
  • Frederic Perez
  • Pere-Pau Vázquez
Original Article

Abstract

Medical datasets are continuously increasing in size. Although larger models may be available for certain research purposes, in the common clinical practice the models are usually of up to \(512 \times 512 \times 2000\) voxels. These resolutions exceed the capabilities of conventional GPUs, the ones usually found in the medical doctors’ desktop PCs. Commercial solutions typically reduce the data by downsampling the dataset iteratively until it fits the available target specifications. The data loss reduces the visualization quality and this is not commonly compensated with other actions that might alleviate its effects. In this paper, we propose adaptive transfer functions, an algorithm that improves the transfer function in downsampled multiresolution models so that the quality of renderings is highly improved. The technique is simple and lightweight, and it is suitable, not only to visualize huge models that would not fit in a GPU, but also to render not-so-large models in mobile GPUs, which are less capable than their desktop counterparts. Moreover, it can also be used to accelerate rendering frame rates using lower levels of the multiresolution hierarchy while still maintaining high-quality results in a focus and context approach. We also show an evaluation of these results based on perceptual metrics.

Keywords

Transfer function Multiresolution volume model Direct volume rendering Level of detail 

Supplementary material

Supplementary material 1 (mp4 25284 KB)

References

  1. 1.
    Balsa Rodríguez, M., Gobbetti, E., Iglesias Guitián, J.A., Makhinya, M., Marton, F., Pajarola, R., Suter, S.: State-of-the-art in compressed GPU-based direct volume rendering. Comput. Graph. Forum 33(6), 77–100 (2014)CrossRefGoogle Scholar
  2. 2.
    Bergner, S., Möller, T., Weiskopf, D., Muraki, D.J.: A spectral analysis of function composition and its implications for sampling in direct volume visualization. IEEE Trans. Vis. Comput. Graph. 12(5), 1353–1360 (2006)CrossRefGoogle Scholar
  3. 3.
    Beyer, J., Hadwiger, M., Möller, T., Fritz, L.: Smooth mixed-resolution gpu volume rendering. In: Proceedings of the Fifth Eurographics/IEEE VGTC conference on Point-Based Graphics, pp. 163–170. Eurographics Association, Los Angeles, CA, USA (2008)Google Scholar
  4. 4.
    Beyer, J., Hadwiger, M., Pfister, H.: A survey of GPU-based large-scale volume visualization. In: Proceedings EuroVis 2014 (2014)Google Scholar
  5. 5.
    Boada, I., Navazo, I., Scopigno, R.: Multiresolution volume visualization with a texture-based octree. Vis. Comput. 17(3), 185–197 (2001)CrossRefMATHGoogle Scholar
  6. 6.
    Crassin, C., Neyret, F., Lefebvre, S., Eisemann, E.: Gigavoxels: ray-guided streaming for efficient and detailed voxel rendering. In: Proceedings of the 2009 Symposium on Interactive 3D Graphics and Games (I3D’09), pp. 15–22. ACM, New York (2009)Google Scholar
  7. 7.
    Fisher, M., Dorgham, O., Laycock, S.D.: Fast reconstructed radiographs from octree-compressed volumetric data. Int. J. Comput. Assist. Radiol. Surg. 8(2), 313–322 (2013)CrossRefGoogle Scholar
  8. 8.
    Fogal, T., Schiewe, A., Krüger, J.: An analysis of scalable GPU-based ray-guided volume rendering. In: 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), pp. 43–51. IEEE (2013)Google Scholar
  9. 9.
    Gobbetti, E., Iglesias Guitián, J., Marton, F.: A compression-domain output-sensitive volume rendering architecture based on a sparse representation of voxel blocks. Comput. Graph. Forum 31(3pt4), 1315–1324 (2012). (Proc. EuroVis 2012)CrossRefGoogle Scholar
  10. 10.
    Gobbetti, E., Marton, F., Iglesias Guitián, J.: A single-pass GPU ray casting framework for interactive out-of-core rendering of massive volumetric datasets. Vis. Comput. 24(7–9), 797–806 (2008). (Proc. CGI 2008)CrossRefGoogle Scholar
  11. 11.
    Guthe, S., Straßer, W.: Advanced techniques for high-quality multi-resolution volume rendering. Comput. Graph. 28(1), 51–58 (2004)CrossRefGoogle Scholar
  12. 12.
    Hadwiger, M., Kniss, J.M., Rezk-salama, C., Weiskopf, D., Engel, K.: Real-Time Volume Graphics. A. K. Peters Ltd, Natick (2006)Google Scholar
  13. 13.
    Jankun-Kelly, T., Ma, K.L.: A study of transfer function generation for time-varying volume data. In: Proceedings of the 2001 Eurographics conference on Volume Graphics, pp. 51–66. Eurographics Association, New York, USA (2001)Google Scholar
  14. 14.
    Kitware, Inc.: VES, the VTK OpenGL ES rendering toolkit (2014)Google Scholar
  15. 15.
    Knoll, A., Thelen, S., Wald, I., Hansen, C.D., Hagen, H., Papka, M.E.: Full-resolution interactive cpu volume rendering with coherent bvh traversal. In: Proc. IEEE Pacific Visualization Symposium, pp. 3–10. IEEE Computer Society, Washington, DC (2011)Google Scholar
  16. 16.
    Knoll, A.M., Wald, I., Hansen, C.D.: Coherent multiresolution isosurface ray tracing. Vis. Comput. 25(3), 209–225 (2009)CrossRefGoogle Scholar
  17. 17.
    Kraus, M., Bürger, K.: Interpolating and downsampling RGBA volume data. In: Proceedings of Vision, Modeling, and Visualization’08, pp. 323–332 (2008)Google Scholar
  18. 18.
    LaMar, E., Hamann, B., Joy, K.I.: Multiresolution techniques for interactive texture-based volume visualization. In: Proceedings of the Conference on Visualization’99: Celebrating Ten Years, VIS’99, pp. 355–361. IEEE Computer Society Press: Los Alamitos (1999)Google Scholar
  19. 19.
    Ljung, P., Lundstrom, C., Ynnerman, A., Museth, K.: Transfer function based adaptive decompression for volume rendering of large medical data sets. In: Proceedings of the 2004 IEEE Symposium on Volume Visualization and Graphics, VV’04, pp. 25–32. IEEE Computer Society, Washington, DC (2004)Google Scholar
  20. 20.
    Martin, S., Shen, H.W.: Interactive transfer function design on large multiresolution volumes. In: 2012 IEEE Symposium on Large Data Analysis and Visualization (LDAV), pp. 19–22. IEEE (2012)Google Scholar
  21. 21.
    Mobeen, M.M., Feng, L.: Ubiquitous medical volume rendering on mobile devices. In: International Conference on Information Society, pp. 93–98. IEEE (2012)Google Scholar
  22. 22.
    Moser, M., Weiskopf, D.: Interactive volume rendering on mobile devices. In: In Vision, Modeling, and Visualization VMV 2008 Conference Proceedings, pp. 217–226 (2008)Google Scholar
  23. 23.
    OsiriX Imaging Software: OsiriX HD (2014)Google Scholar
  24. 24.
    Raster Images: Oviyam—Web DICOM browser (2014)Google Scholar
  25. 25.
    Ruiz, M., Bardera, A., Boada, I., Viola, I., Feixas, M., Sbert, M.: Automatic transfer functions based on informational divergence. IEEE Trans. Vis. Comput. Graph. 17(12), 1932–1941 (2011)CrossRefGoogle Scholar
  26. 26.
    Šereda, P., Vilanova, A., Gerritsen, F.A.: Automating transfer function design for volume rendering using hierarchical clustering of material boundaries. In: Proc. of Eurographics/IEEE VGTC Conference on Visualization, pp. 243–250. Eurographics Association, Lisbon, Portugal (2006)Google Scholar
  27. 27.
    Sicat, R., Hadwiger, M., Krüger, J., Möller, T.: Sparse PDF volumes for consistent multi-resolution volume rendering. IEEE Trans. Vis. Comput. Graph. (Proc. IEEE Vis.) 20(12), 2417–2426 (2014)CrossRefGoogle Scholar
  28. 28.
    Sousa, R., Nisi, V., Oakley, I.: Glaze: A visualization framework for mobile devices. In: Human–Computer Interaction–INTERACT, pp. 870–873. Springer, New York (2009)Google Scholar
  29. 29.
    Thelen, S., Meyer, J., Ebert, A., Hagen, H.: Giga-scale multiresolution volume rendering on distributed display clusters. In: Human Aspects of Visualization, pp. 142–162. Springer, New York (2011)Google Scholar
  30. 30.
    Wang, C., Gao, J., Li, L., Shen, H.W.: A multiresolution volume rendering framework for large-scale time-varying data visualization. In: Proceedings of the Fourth Eurographics/IEEE VGTC Conference on Volume Graphics, pp. 11–19. Eurographics Association, New York, USA (2005)Google Scholar
  31. 31.
    Wang, Y.S., Wang, C., Lee, T.Y., Ma, K.L.: Feature-preserving volume data reduction and focus \(+\) context visualization. IEEE Trans. Vis. Comput. Graph. 17(2), 171–181 (2011)CrossRefGoogle Scholar
  32. 32.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  33. 33.
    Weiler, M., Westermann, R., Hansen, C., Zimmermann, K., Ertl, T.: Level-of-detail volume rendering via 3D textures. In: Proceedings of the 2000 IEEE Symposium on Volume Visualization, VVS’00, pp. 7–13. ACM, New York (2000)Google Scholar
  34. 34.
    Xu, X., Sakhaee, E., Entezari, A.: Volumetric data reduction in a compressed sensing framework. Comput. Graph. Forum 33(3), 111–120 (2014)CrossRefGoogle Scholar
  35. 35.
    Younesy, H., Möller, T., Carr, H.: Improving the quality of multi-resolution volume rendering. In: Proc. Joint Eurographics/IEEE VGTC Conference on Visualization, pp. 251–258. Eurographics Association, Lisboa, Portugal (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jesús Díaz-García
    • 1
    • 2
  • Pere Brunet
    • 1
  • Isabel Navazo
    • 1
  • Frederic Perez
    • 2
  • Pere-Pau Vázquez
    • 1
  1. 1.Universitat Politècnica de CatalunyaBarcelonaSpain
  2. 2.Alma IT SystemsBarcelonaSpain

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