Hierarchical Cell Structures for Segmentation of Voxel Images

  • Lutz Priese
  • Patrick Sturm
  • Haojun Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

We compare three hierarchical structures, S 15, C 15, C 19, that are used to steer a segmentation process in 3d voxel images. There is an important topological difference between C 19 and both others that we will study. A quantitative evaluation of the quality of the three segmentation techniques based on several hundred experiments is presented.

Keywords

Island Structure Neighbor Island Partial Segment Color Structure Code Dense Sphere Packing 
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.

References

  1. 1.
    Dokladal, P., Urtasun, R., Bloch, I., Garnero, L.: Segmentation of 3d head mr images using morphological reconstruction under constraints and automatic selection of markers. In: IEEE International Conference on Image Processing 2001, Thessalonique, Greece. ICIP 2001, vol. III, pp. 1075–1078 (2001)Google Scholar
  2. 2.
    Sijbers, J., Scheunders, P., Verhoye, M., Van der Linden, A., Van Dyck, D., Raman, E.: Watershed-based segmentation of 3d mr data for volume quantization. Magnetic Resonance Imaging 15(6), 679–688 (1997)CrossRefGoogle Scholar
  3. 3.
    Baillard, C., Hellier, P., Barillot, C.: Segmentation of brain 3d mr images using level sets and dense registration. Med. Image Anal. 5(3), 185–194 (2001)CrossRefGoogle Scholar
  4. 4.
    Stammberger, T., Rudert, S., Michaelis, M., Reiser, M., Englmeier, K.-H.: Segmentation of mr images with b-spline snakes. a multi-resolution approach using the distance transformation for model forces. In: Bildverarbeitung für die Medizin. Springer, Heidelberg (1998)Google Scholar
  5. 5.
    Werner, C.D., Sachse, F.B., Mühlmann, K., Dössel, O.: Modellbasierte segmentation klinischer mr-aufnahmen. In: Bildverarbeitung für die Medizin. Springer, Berlin (1998)Google Scholar
  6. 6.
    Priese, L., Rehrmann, V.: A fast hybrid color segmentation method. In: Pöppl, S.J., Handels, H. (eds.) Proc. DAGM Symposium Mustererkennung, Informatik Fachberichte, pp. 297–304. Springer, Heidelberg (1993)Google Scholar
  7. 7.
    Hartmann, G.: Recognition of hierarchically encoded images by technical and biological systems. Biologicak Cybernetics 57, 73–84 (1987)CrossRefGoogle Scholar
  8. 8.
    Sturm, P., Priese, L.: 3d-color-structure-code. A hierarchical region growing method for segmentation of 3d-images. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 603–608. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Harwood, D., Subbararao, M., Hakalahti, H., Davis, L.: A new class of edge preserving smoothing filters. Pattern Recongition Letters 2, 155–162 (1987)CrossRefGoogle Scholar
  10. 10.
    Kuwahara, M., Hachimura, K., Eiho, S., Kinoshita, M.: Processing of ri-angiocardiographic images. In: Preston, K., Onoe, M. (eds.) Digital Processing of Biomedical Images, pp. 187–202 (1976)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Lutz Priese
    • 1
  • Patrick Sturm
    • 1
  • Haojun Wang
    • 1
  1. 1.Institute for Computational VisualisticsUniversity Koblenz-LandauKoblenzGermany

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