Hierarchical σ–Octree for Visualization of Ultrasound Datasets

  • Sukhyun Lim
  • Byeong-Seok Shin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


There are two important factors to visualize ultrasound datasets using volume ray casting method. Firstly, efficient methods to skip over empty space are required. Secondly, adequate noise-detection methods are necessary because ultrasound datasets contain lots of speckle noises. In general, space-leaping and noise-filtering methods are exploited to solve the problems. However, it increases the preprocessing time to generate the filtered datasets, and interesting (meaningful) objects could be affected by a filtering operation. We propose a hierarchical octree containing min-max values and standard deviation for each block, named a hierarchical σ–octree. In rendering step, our method refers to min-max values of a block. If the block is regarded as nontransparent, it also checks its standard deviation value to detect speckle noises. Our method reduces rendering time compared with the method using only the min-max values because most blocks containing speckle noises are considered as transparent.


Speckle Noise Interesting Object Transparent Region Volume Dataset Volume Visualization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Krishnamurthy, B., Bajaj, C.: Data Visualization Techniques, 1st edn. John Wiley & Sons, Chichester (1999)Google Scholar
  2. 2.
    Kaufman, A.: Volume Visualization, 1st edn. IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
  3. 3.
    Baba, K., Jurkovic, D.: Three-Dimensional Ultrasound in Obstetrics and Gynecology. Progress in Obstetric and Gynecological Sonography Series. The Parthenon Publishing Group (1997)Google Scholar
  4. 4.
    Fattal, R., Lischinski, D.: Variational Classification for Visualization of 3D Ultrasound Data. In: Proc. Visualization 2001, pp.403-410 (2001) Google Scholar
  5. 5.
    Sakas, G., Walter, S.: Extracting Surfaces from Fuzzy 3D-Ultrasound Data. In: Cook, R. (ed.) Proc. SIGGRAPH 1995, pp. 465–474 (1995)Google Scholar
  6. 6.
    Sakas, G., Schreyer, L.A., Grimm, M.: Preprocessing and Volume Rendering of 3D Ultra-sonic Data. IEEE Computer Graphics and Applications 15(4), 47–54 (1995)CrossRefGoogle Scholar
  7. 7.
    Shamdasani, V., Bae, U., Managuli, R., Kim, Y.: Improving the Visualization of 3D Ultra-sound Data with 3D Filtering. In: Proc. SPIE, vol. 5744, pp. 455–461 (2005)Google Scholar
  8. 8.
    Levoy, M.: Efficient Ray Tracing of Volume Data. ACM Transactions on Graphics 9, 245–261 (1990)MATHCrossRefGoogle Scholar
  9. 9.
    Danskin, J., Hanrahan, P.: Fast Algorithms for Volume Ray Tracing. In: Proc. Volume visualization 1992, pp. 91–98 (1992)Google Scholar
  10. 10.
    Wilhelms, J., Gelder, A.V.: Octree for Faster Isosurface Generation. ACM Transactions on Graphics 11(3), 201–227 (1992)MATHCrossRefGoogle Scholar
  11. 11.
    Parker, S., Shirley, P., Livnat, Y., Hansen, C., Sloan, P.: Interactive Ray Tracing for Iso-surface Rendering. In: Proc. IEEE Visualization 1998, pp. 233–238 (1998)Google Scholar
  12. 12.
    Knittel, G.: The UltraVis System. In: Proc. IEEE Volume Visualization 2000, pp. 71–79 (2000)Google Scholar
  13. 13.
    Mora, B., Jessel, J., Caubet, R.: A New Object Order Ray-casting Algorithm. In: Proc. IEEE Volume Visualization 2002, pp. 203–210 (2002)Google Scholar
  14. 14.
    Grimm, S., Bruckner, S., Kanitsar, A., Gröller, E.: Memory Efficient Acceleration Struc-tures and Techniques for CPU-based Volume Raycasting of Large Data. In: Proc. IEEE Volume Visualization 2004, pp. 1–8 (2004)Google Scholar
  15. 15.
    Lim, S., Shin, B.: Reliable Space Leaping Using Distance Template. In: Barreiro, J.M., Martín-Sánchez, F., Maojo, V., Sanz, F. (eds.) ISBMDA 2004. LNCS, vol. 3337, pp. 60–66. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Hadwiger, M., Sigg, C., Scharsach, H., Buhler, K., Gross, M.: Real-time Ray-casting and Advanced Shading of Discrete Isosurfaces. Graphics Forum 24(3), 303–312 (2005)CrossRefGoogle Scholar
  17. 17.
    Lim, S., Shin, B.: RPO: A Reverse-Phased Hierarchical Min-Max Octree for Efficient Space-Leaping. In: Proc. Pacific Graphics 2005, pp. 145–147 (2005)Google Scholar
  18. 18.
    Lim, S., Shin, B.: Efficient Space-Leaping Using Optimal Block Sets. IEICE Transactions on Information and Systems E88-D(12), 2864–2870 (2005)CrossRefGoogle Scholar
  19. 19.
    Yagel, R., Cohen, D., Kaufman, A.: Discrete Ray Tracing. IEEE Computer Graphics and Applications 12(5), 19–28 (1992)CrossRefGoogle Scholar
  20. 20.
    Medison, co., ltd, Rep. of Korea, http://www.medison.com/eng/index.asp
  21. 21.
    Cohen, D., Sheffer, Z.: Proximity Clouds: An Acceleration Technique for 3D Grid Traversal. The Visual Computer 11(1), 27–28 (1994)CrossRefGoogle Scholar
  22. 22.
    Sramek, M., Kaufman, A.: Fast Ray-tracing of Rectilinear Volume Data Using Distance Transforms. IEEE Trans. on Visualization and Computer graphics 6(3), 236–252 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sukhyun Lim
    • 1
  • Byeong-Seok Shin
    • 1
  1. 1.Dept. Computer Science and Information EngineeringInha UniversityInchonRep. of Korea

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