Advertisement

Medical Image Segmentation Using Topologically Adaptable Snakes

  • Tim McInerney
  • Demetri Terzopoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 905)

Abstract

This paper presents a technique for the segmentation of anatomic structures in medical images using a topologically adaptable snakes model. The model is set in the framework of domain subdivision using simplicial decomposition. This framework allows the model to maintain all of the strengths associated with traditional snakes while overcoming many of their limitations. The model can flow into complex shapes, even shapes with significant protrusions or branches, and topological changes are easily sensed and handled. Multiple instances of the model can be dynamically created, can seamlessly split or merge, or can simply and quickly detect and avoid collisions. Finally, the model can be easily and dynamically converted to and from the traditional parametric snakes model representation. We apply a 2D model to segment structures from medical images with complex shapes and topologies, such as arterial “trees”, that cannot easily be segmented with traditional deformable models.

Keywords

Collision Detection Interior Region Active Contour Model Model Node Simplicial Decomposition 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M. O. Berger and R. Mohr. Towards Autonomy in Active Contour Models. In Proc. of Tenth International Conference on Pattern Recognition, pages 847–851, 1990.CrossRefGoogle Scholar
  2. 2.
    I. Carlbom, D. Terzopoulos, and K. Harris. Computer-assisted registration, segmentation, and 3D reconstruction from images of neuronal tissue sections. IEEE Transactions on Medical Imaging, 13 (2): 351–362, 1994.CrossRefGoogle Scholar
  3. 3.
    L.D. Cohen. On active contour models and balloons. In CVGIP: Image Understanding, volume 53(2), pages 211–218, March 1991.CrossRefzbMATHGoogle Scholar
  4. 4.
    M. Kass, A. Witkin, and D. Terzopoulo. Snakes: Active contour models. International Journal of Computer Vision, pages 321–331, 1988.Google Scholar
  5. 5.
    R. Malladi, J. Sethian, and B. Vemuri. Shape modeling with front propagation: A level set approach. IEEE Trans. Pattern Analysis and Machine Intelligence. In Press.Google Scholar
  6. 6.
    R. Samadani. Changes in connectivity in active contour models. In Proceedings of the Workshop on Visual Motion, pages 337–343, March 1989.CrossRefGoogle Scholar
  7. 7.
    D. Terzopoulos, A. Witkin, and M. Kass. Constraints on deformable models: Recovering 3D shape and nonrigid motion. Artificial Intelligence, 36 (1): 91–123, 1988.CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Tim McInerney
    • 1
  • Demetri Terzopoulos
    • 1
  1. 1.Dept. of Computer ScienceUniversity of TorontoTorontoCanada

Personalised recommendations