Skip to main content
SpringerLink
Log in

A clustering-based system to automate transfer function design for medical image visualization

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Finding good transfer functions for rendering medical volumes is difficult, non-intuitive, and time-consuming. We introduce a clustering-based framework for the automatic generation of transfer functions for volumetric data. The system first applies mean shift clustering to oversegment the volume boundaries according to their low-high (LH) values and their spatial coordinates, and then uses hierarchical clustering to group similar voxels. A transfer function is then automatically generated for each cluster such that the number of occlusions is reduced. The framework also allows for semi-automatic operation, where the user can vary the hierarchical clustering results or the transfer functions generated. The system improves the efficiency and effectiveness of visualizing medical images and is suitable for medical imaging applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bajaj, C.L., Pascucci, V., Schikore, D.R.: The contour spectrum. In: Proceedings of IEEE Visualization, pp. 167–173 (1997)

    Google Scholar 

  2. Chan, M.Y., Wu, Y., Mak, W.H., Chen, W., Qu, H.: Perception-based transparency optimization for direct volume rendering. IEEE Trans. Vis. Comput. Graph. 15(6), 1077–2626 (2009)

    Google Scholar 

  3. Correa, C.D., Ma, K.L.: Size-based transfer functions: a new volume exploration technique. IEEE Trans. Vis. Comput. Graph. 14(6), 1380–1387 (2008)

    Article  Google Scholar 

  4. Correa, C.D., Ma, K.L.: The occlusion spectrum for volume visualization and classification. IEEE Trans. Vis. Comput. Graph. 15(6), 1465–1472 (2009)

    Article  Google Scholar 

  5. Correa, C.D., Ma, K.L.: Visibility driven transfer functions. In: IEEE Pacific Visualization Symposium, pp. 177–184 (2009)

    Chapter  Google Scholar 

  6. Correa, C.D., Ma, K.L.: Visibility histograms and visibility-driven transfer functions. IEEE Trans. Vis. Comput. Graph. 17(2), 1077–2626 (2010)

    Google Scholar 

  7. Duda, R.O., Ehart, P., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2001)

    MATH  Google Scholar 

  8. Fukunaga, K., Larry, H.D.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21(1), 32–40 (1975)

    Article  MATH  Google Scholar 

  9. Hadwiger, M., Fritz, L., Rezk-Salama, C., Höllt, T., Geier, G., Pabel, T.: Interactive volume exploration for feature detection and quantification in industrial CT data. IEEE Trans. Vis. Comput. Graph. 14(6), 1507–1514 (2008)

    Article  Google Scholar 

  10. Hladuvka, J., König, A., Gröller, E.: Curvature-based transfer functions for direct volume rendering. In: Proceedings of Spring Conference on Computer Graphics, pp. 58–65 (2000)

    Google Scholar 

  11. Hong, D., Ning, G., Zhao, T., Zhang, M., Zheng, X.: Method of normal estimation based on approximation for visualization. J. Electron. Imaging 12(3), 470–477 (2003)

    Article  Google Scholar 

  12. Huang, R., Ma, K.L.: RGVis: region growing based techniques for volume visualization. In: Proceedings of Pacific Conference on Computer Graphics and Applications, pp. 355–363 (2003)

    Google Scholar 

  13. Kindlmann, G.: Transfer functions in direct volume rendering: design, interface, interaction. In: Course Note of ACM SIGGRAPH (2002)

    Google Scholar 

  14. Kindlmann, G., Durkin, J.W.: Semi-automatic generation of transfer functions for direct volume rendering. In: Proceedings of IEEE Symposium on Volume Visualization, pp. 79–86 (1998)

    Chapter  Google Scholar 

  15. Kindlmann, G., Whitaker, R., Tasdizen, T., Möller, T.: Curvature-based transfer functions for direct volume rendering: methods and applications. In: Proceedings of IEEE Symposium on Volume Visualization, pp. 513–520 (2003)

    Google Scholar 

  16. Kniss, J., Kindlmann, G., Hansen, C.: Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets. In: Proceedings of IEEE Symposium on Volume Visualization, pp. 255–262 (2001)

    Google Scholar 

  17. Kniss, J., Kindlmann, G., Hansen, C.: Multi-dimensional transfer functions for interactive volume rendering. IEEE Trans. Vis. Comput. Graph. 8(3), 270–285 (2002)

    Article  Google Scholar 

  18. Lum, E.B., Ma, K.L.: Lighting transfer functions using gradient aligned sampling. In: Proceedings of IEEE Visualization, pp. 289–296 (2004)

    Google Scholar 

  19. Lundström, C., Ljung, P., Ynnerman, A.: Extending and simplifying transfer function design in medical volume rendering using local histograms. In: Proceedings of IEEE/Eurographics Symposium on Visualization, pp. 263–270 (2005)

    Google Scholar 

  20. Lundström, C., Ljung, P., Ynnerman, A.: Local histograms for design of transfer functions in direct volume rendering. IEEE Trans. Vis. Comput. Graph. 12(6), 1570–1579 (2006)

    Article  Google Scholar 

  21. Lundström, C., Ynnerman, A., Ljung, P., Persson, A., Knutsson, H.: The α-histogram: using spatial coherence to enhance histograms and transfer function design. In: Proceedings of IEEE/Eurographics Symposium on Visualization, pp. 227–234 (2006)

    Google Scholar 

  22. Maciejewski, R., Chen, W., Woo, I., Ebert, D.S.: Structuring feature space—a non-parametric method for volumetric transfer function generation. IEEE Trans. Vis. Comput. Graph. 15(6), 1473–1480 (2009)

    Article  Google Scholar 

  23. de Moura Pinto, F., Freitas, C.M.D.S.: Design of multi-dimensional transfer functions using dimensional reduction. In: Proceedings of IEEE/Eurographics Symposium on Visualization, pp. 131–138 (2007)

    Google Scholar 

  24. Nguyen, B.P., Tay, W.L., Chui, C.K., Ong, S.H.: Automatic transfer function design for volumetric data visualization using clustering on LH space. In: Proceedings of Computer Graphics International (2011)

    Google Scholar 

  25. Pekar, V., Wiemker, R., Hempel, D.: Fast detection of meaningful isosurfaces for volume data visualization. In: Proceedings of IEEE Visualization, pp. 223–230 (2001)

    Google Scholar 

  26. Praßni, J.S., Ropinski, T., Hinrichs, K.H.: Efficient boundary detection and transfer function generation in direct volume rendering. In: Proceedings of the 14th International Fall Workshop on Vision, Modeling, and Visualization (VMV09), pp. 285–294 (2009)

    Google Scholar 

  27. Röttger, S., Bauer, M., Stamminger, M.: Spatialized transfer functions. In: Proceedings of IEEE/Eurographics Symposium on Visualization, pp. 271–278 (2005)

    Google Scholar 

  28. Tappenbeck, A., Preim, B., Dicken, V.: Distance-based transfer function design: specification methods and applications. In: Proceedings of Simulation und Visualisierung, pp. 259–274 (2006)

    Google Scholar 

  29. Teistler, M., Breiman, R.S., Liong, S.M., Ho, L.Y., Shahab, A., Nowinski, W.L.: Interactive definition of transfer functions in volume rendering based on image markers. Int. J. Comput. Assisted Radiol. Surg. 2(1), 55–64 (2007)

    Article  Google Scholar 

  30. Tzeng, F.Y., Ma, K.L.: A cluster-space visual interface for arbitrary dimensional classification of volume data. In: Proceedings of IEEE/Eurographics Symposium on Visualization, pp. 17–24 (2004)

    Google Scholar 

  31. Tzeng, F.Y., Lum, E.B., Ma, K.L.: A novel interface for higher-dimensional classification of volume data. In: Proceedings of IEEE Visualization, pp. 505–512 (2003)

    Google Scholar 

  32. Tzeng, F.Y., Lum, E.B., Ma, K.L.: An intelligent system approach to higher-dimensional classification of volume data. IEEE Trans. Vis. Comput. Graph. 11(3), 273–284 (2005)

    Article  Google Scholar 

  33. Šereda, P., Bartroli, A.V., Serlie, I.W.O., Gerritsen, F.A.: Visualization of boundaries in volumetric data sets using LH histograms. IEEE Trans. Vis. Comput. Graph. 12(2), 208–218 (2006)

    Article  Google Scholar 

  34. Šereda, P., Vilanova, A., Gerritsen, F.A.: Automating transfer function design for volume rendering using hierarchical clustering of material boundaries. In: Proceedings of IEEE/Eurographics Symposium on Visualization, pp. 243–250 (2006)

    Google Scholar 

  35. Wesarg, S., Kirschner, M.: Structure size enhanced histogram. In: Brauer, W., Meinzer, H.P., Deserno, T.M., Handels, H., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2009. Informatik aktuell, pp. 16–20. Springer, Berlin (2009)

    Chapter  Google Scholar 

  36. Wesarg, S., Kirschner, M., Khan, M.F.: 2D histogram based volume visualization: combining intensity and size of anatomical structures. Int. J. Comput. Assist. Radiol. Surg. 5(6), 655–666 (2010)

    Article  Google Scholar 

  37. Zhou, F., Zhao, Y., Ma, K.: Parallel mean shift for interactive volume segmentation. Mach. Learn. Med. Imaging, 67–75 (2010)

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Binh P. Nguyen & Wei-Liang Tay

  2. Department of Electrical and Computer Engineering, and Division of Bioengineering, National University of Singapore, Singapore, Singapore

    Sim-Heng Ong

  3. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

    Chee-Kong Chui

Authors
  1. Binh P. Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei-Liang Tay
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chee-Kong Chui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sim-Heng Ong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Binh P. Nguyen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nguyen, B.P., Tay, WL., Chui, CK. et al. A clustering-based system to automate transfer function design for medical image visualization. Vis Comput 28, 181–191 (2012). https://doi.org/10.1007/s00371-011-0634-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-011-0634-3

Keywords

Search

Navigation