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Delaunay-Based Vector Segmentation of Volumetric Medical Images

  • Michal Španěl
  • Přemysl Kršek
  • Miroslav Švub
  • Vít Štancl
  • Ondřej Šiler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

The image segmentation plays an important role in medical image processing. Many segmentation algorithms exist. Most of them produce raster data which is not suitable for 3D geometrical modeling of human tissues. In this paper, a vector segmentation algorithm based on a 3D Delaunay triangulation is proposed. Tetrahedral mesh is used to divide a volumetric CT/MR data into non-overlapping regions whose characteristics are similar. Novel methods for improving quality of the mesh and its adaptation to the image structure are also presented.

Keywords

Feature Vector Delaunay Triangulation Iterative Adaptation Image Edge Deformable Model 
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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References

  1. 1.
    Kršek, P.: Direct Creating of FEM Models from CT/MR Data for Biomechanics Applications. PhD thesis, Vutium, Brno University of Technology, Brno, Czech Republic (2001)Google Scholar
  2. 2.
    McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: A survey. Medical Image Analysis (1996)Google Scholar
  3. 3.
    Cohen, L., Cohen, I.: Finite element methods for active contour models and baloons for 2d and 3d images. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1131–1147 (1993)CrossRefGoogle Scholar
  4. 4.
    Lachaud, J.O., Montanvert, A.: Volumic segmentation using hierarchical representation and triangulated surface. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 137–146. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  5. 5.
    Davoine, F., Chassery, J.M.: Adaptive Delaunay triangulation for attractor image coding. In: Proceedings of the 12th International Conference on Pattern Recognition, Jerusalem, Israel, pp. 801–803 (1994)Google Scholar
  6. 6.
    Gevers, T.: Adaptive image segmentation by combining photometric invariant region and edge information. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002)Google Scholar
  7. 7.
    Owen, S.: A survey of unstructured mesh generation technology. In: Proceedings of the Seventh International Meshing Roundtable, Dearborn, Michigan, Sandia National Laboratories (October 1998)Google Scholar
  8. 8.
    George, P.L., Borouchaki, H.: Delaunay Triangulation and Meshing: Application to Finite Elements. Editions HERMES, Paris, France (1998)Google Scholar
  9. 9.
    Pham, D.L., Prince, J.L.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Transactions on Medical Imaging 18 (September 1999)Google Scholar
  10. 10.
    Ng, S.K., McLachlan, G.J.: On some variants of the em algorithm for fitting mixture models. Austrian Journal of Statistics 23, 143–161 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michal Španěl
    • 1
  • Přemysl Kršek
    • 1
  • Miroslav Švub
    • 1
  • Vít Štancl
    • 1
  • Ondřej Šiler
    • 1
  1. 1.Department of Computer Graphics and Multimedia, Faculty of Information Technology, Brno University of TechnologyCzech Republic

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