A Level Set Approach for Shape Recovery of Open Contours

  • Min Li
  • Chandra Kambhamettu
  • Maureen Stone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3851)


In this paper, a geometric deformable model for shape recovery of open contours in noisy images is presented. We use two level set functions to model the open contour and find the end points of the open contour as the intersection of the two level set functions. The evolutions of both level set functions do not depend on the gradient of the images, as in the classical geometric deformable models, but are decided by a region-based ”band velocity”. The ”band velocity” is different from region information introduced by other deformable models which can only be used to find the closed contours in images, it is designed for evolutions of both closed and open contours and particularly unique for contours which are open and do not enclose any region. Prior shape information is also integrated into the contour evolution process, which prevents two level set functions from intersecting at other places than at the contour end points. With the described method open contours can be recovered from noisy images. Successful experiments on several data sets are presented in this paper.


Vocal Tract Noisy Image Shape Recovery Deformable Model Statistical Shape 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. IJCV 22(1), 61–79 (1997)zbMATHCrossRefGoogle Scholar
  2. 2.
    Malladi, R., Sethian, J., Vemuri, B.: Shape modeling with front propagation: A level set approach. PAMI 17, 158–175 (1995)Google Scholar
  3. 3.
    Han, X., Xu, C., Prince, J.: Topology preserving level set method for geometric deformable models. PAMI 25, 755–768 (2003)Google Scholar
  4. 4.
    Niethammer, M., Tannenbaum, A.: Dynamic geodesic snakes for visual tracking. In: CVPR 2004, pp. I: 660–667 (2004)Google Scholar
  5. 5.
    Chan, T., Vese, L.: Active contours without edges. IP 10, 266–277 (2001)zbMATHGoogle Scholar
  6. 6.
    Leventon, M., Grimson, W., Faugeras, O.: Statistical shape influence in geodesic active contours. In: CVPR 2000, pp. I: 316–323 (2000)Google Scholar
  7. 7.
    Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  8. 8.
    Smereka, P.: Spiral crystal growth. Physica D 138, 282–301 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Solem, J., Heyden, A.: Reconstructing open surfaces from unorganized data points. In: CVPR 2004, pp. II: 653–660 (2004)Google Scholar
  10. 10.
    Bertalmio, M., Sapiro, G., Randall, G.: Region tracking on level-sets methods. MedImg 18, 448–451 (1999)Google Scholar
  11. 11.
    Stone, M.: A guide to analyzing tongue motion from ultrasound images. International Journal of Clinical Linguistics and Phonetics 19, 455–502 (2005)CrossRefGoogle Scholar
  12. 12.
    Chalana, V., Costa, W.S., Kim, Y.: Integrating region growing and edge detection using regularization. In: Loew, M.H. (ed.) Medical Imaging 1995: Image Processing. Proc. SPIE, vol. 2434, pp. 262–271 (1995)Google Scholar
  13. 13.
    Caselles, V., Catte, F., Coll, T., Dibos, F.: A geometric model for active contours in image processing. Number. Math. 66, 1–31 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Parthasarathy, V., Stone, M., Prince, J.L.: Spatiotemporal visualization of the tongue using ultrasound and kriging. In: Proc. Of SPIE-Medical Imaging (2003)Google Scholar
  16. 16.
    Li, M., Kambhamettu, C., Stone, M.: Tongue motion averaging from contour sequences. International Journal of Clinical Linguistics and Phonetics 19, 515–528 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Min Li
    • 1
  • Chandra Kambhamettu
    • 1
  • Maureen Stone
    • 2
  1. 1.Department of Computer & Information SciencesUniversity of DelawareNewarkUSA
  2. 2.Dept of Biomedical Sciences and Orthodontics, Vocal Tract Visualization LabUniversity of Maryland Dental SchoolBaltimoreUSA

Personalised recommendations