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Cluster Analysis in Application to Quantitative Inspection of 3D Vascular Tree Images

  • Artur Klepaczko
  • Marek Kocinski
  • Andrzej Materka
Chapter
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)

Summary

This paper provides — through the use of cluster analysis — objective confirmation of the relevance of texture description applied to vascular tree images. Moreover, it is shown that unsupervised selection of significant texture parameters in the datasets corresponding to noisy images becomes feasible if the search for relevant attributes is guided by the clustering stability–based optimization criterion.

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References

  1. 1.
    Struyf, A., Hubert, M., Rousseeuw, P.J.: Computational Statistics & Data Analysis 26, 17–37 (1997)Google Scholar
  2. 2.
    Keener, J., Sneyd, J.: Mathematical Physiology. Springer, Berlin (1998)Google Scholar
  3. 3.
    Bezy-Wendling, J., Kretowski, M., Rolland, Y.: Biology and Med. 33, 77–89 (2003)Google Scholar
  4. 4.
    Karch, R., Neumann, F., Neumann, M., Szawlowski, P.: Annals Biomedical Engineering 31, 548–563 (2003)Google Scholar
  5. 5.
    Kretowski, M., Rolland, Y., Bezy-Wendling, J., Coatrieux, J.-L.: Comput. Methods Programs Biomed. 70, 129–136 (2003)Google Scholar
  6. 6.
    Tofts, P.: Quantitative MRI of the Brain: measuring changed caused by desease. John Wiley & Sons, Chichester (2003)Google Scholar
  7. 7.
    Lange, T., Braun, M.L., Roth, V., Buhmann, J.: Neural Computation 16, 1299–1323 (2004)Google Scholar
  8. 8.
    Oh, I.S., Lee, J.S., Moon, B.R.: IEEE Trans. PAMI 26(11), 1424–1437 (2004)Google Scholar
  9. 9.
    von Luxburg, U., Ben-David, S.: Towards a statistical theory for clustering. In: PASCAL Workshop on Statistics and Optimization of Clustering (2005)Google Scholar
  10. 10.
    Dickie, R.: Microvascular Research, 20–26 (2006)Google Scholar
  11. 11.
    Materka, A.: What is the texture? In: Hajek, M., Dezortova, M., Materka, A., Lerski, R. (eds.) Texture Analysis for Magnetic Resonance Imaging, pp. 11–43. Med4 Publishing, Prague (2006)Google Scholar
  12. 12.
    Kocinski, M., Materka, A., Lundervold, A.: On The Effect of Vascular Tree Parameters on 3D Texture of Its Image. In: Proc. ISMRM-ESMRMB Conference, Berlin (2007)Google Scholar
  13. 13.
    Kocinski, M., Materka, A., Lundervold, A., Chekenya, M.: Classification of Vascular Tree Images on Numerical Descriptors in 3D. In: Tkacz, E., Komorowski, D., Kostka, P., Budzianowski, Z. (eds.) Proc. 9th International Conference SYMBIOSIS 2008, Kamien Slaski (2008)Google Scholar
  14. 14.
    Szczypinski, P., Strzelecki, M., Materka, A., Klepaczko, A.: Comput. Methods Programs Biomed. (2008), doi:10.1016/j.cmpb.2008.08.005Google Scholar
  15. 15.
    Klepaczko, A., Materka, A.: Clustering stability-based feature selection for unsupervised texture classification. Machine Graphics & Vision (in press, 2009)Google Scholar
  16. 16.
    http://www.keyres-technologies.com (2009) (visited: January 2009)
  17. 17.
    http://www.maths.bris.ac.uk/~wavethresh/LS2W (2009) (visited: January 2009)

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Artur Klepaczko
    • 1
  • Marek Kocinski
    • 1
  • Andrzej Materka
    • 1
  1. 1.Technical University of LodzLodzPoland

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