Skip to main content

Piecewise Constant Level Set Methods and Image Segmentation

  • Conference paper
Scale Space and PDE Methods in Computer Vision (Scale-Space 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3459))

Included in the following conference series:

Abstract

In this work we discuss variants of a PDE based level set method. Traditionally interfaces are represented by the zero level set of continuous level set functions. We instead use piecewise constant level set functions, and let interfaces be represented by discontinuities. Some of the properties of the standard level set function are preserved in the proposed method. Using the methods for interface problems, we minimize a smooth locally convex functional under a constraint. We show numerical results using the methods for image segmentation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bertsekas, D.P.: Constrained optimization and Lagrange multiplier methods. Academic Press Inc., London (1982)

    MATH  Google Scholar 

  2. Chambolle, A.: Image segmentation by variational methods: Mumford and Shah functional and the discrete approximations. SIAM J. Appl. Math. 55, 827–863 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  3. Chan, T.F., Tai, X.-C.: Identification of discontinuous coefficients in elliptic problems using total variation regularization. SIAM J. Sci. Comput. 25, 881–904 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  4. Chan, T.F., Tai, X.-C.: Level set and total variation regularization for elliptic inverse problems with discontinuous coefficients. J. Comput. Phys. 193, 40–66 (2003)

    Article  MathSciNet  Google Scholar 

  5. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Im. Proc. 10, 266–277 (2001)

    Article  MATH  Google Scholar 

  6. Cremers, D., Kohlberger, T., Schnörr, C.: Shape statistics in kernel space for variational image segmentation. Patt. Recogn. 36, 1929–1943 (2003)

    Article  MATH  Google Scholar 

  7. Cremers, D., Sochen, N., Schnörr, C.: Multiphase dynamic labeling for variational recognition-driven image segmentation. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 74–86. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  8. Dias Velasco, F.R.: Thresholding using the ISODATA clustering algorithm. IEEE Trans. Systems Man Cybernet. 10, 771–774 (1980)

    Article  MathSciNet  Google Scholar 

  9. Esedoglu, S., Tsai, Y.-H.R.: Threshold dynamics for the piecewise constant mumford-shah functional, UCLA-CAM Report 04-63 (2004)

    Google Scholar 

  10. Fedkiw, R.P., Sapiro, G., Shu, C.-W.: Shock capturing, level sets, and PDE based methods in computer vision and image processing: a review of Osher’s contributions. J. Comput. Phys. 185, 309–341 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  11. Gibou, F., Fedkiw, R.: A fast hybrid k-means level set algorithm for segmentation, Stanford Tech. Rep., 2002 (2002) (in review)

    Google Scholar 

  12. Heath, M.T.: Scientific computing: an introductory survey (2001)

    Google Scholar 

  13. Kunisch, K., Tai, X.-C.: Sequential and parallel splitting methods for bilinear control problems in Hilbert spaces. SIAM J. Numer. Anal. 34, 91–118 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  14. Lie, J., Lysaker, M., Tai, X.-C.: A variant of the level set method and applications to image segmentation, UCLA, CAM-report, 03-50 (2003)

    Google Scholar 

  15. Lie, J., Lysaker, M., Tai, X.-C.: A binary level set model and some applications for mumford-shah image segmentation, UCLA, CAM-report, 04-31 (2004)

    Google Scholar 

  16. Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42, 577–685 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  17. Nocedal, J., Wright, S.J.: Numerical optimization. Series in Operations Research. Springer, Heidelberg (1999)

    Book  MATH  Google Scholar 

  18. Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. In: Appl. Math. Sci., vol. 153. Springer, Heidelberg (2003)

    Google Scholar 

  19. Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79, 12–49 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  20. Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithm. Physica D. 60, 259–268 (1992)

    Article  MATH  Google Scholar 

  21. Samson, C., Blanc-Feraud, L., Aubert, G., Zerubia, J.: A level set model for image classification. IJCV 40, 187–198 (2000)

    Article  MATH  Google Scholar 

  22. Sethian, J.A.: Level set methods and fast marching methods, 2nd edn. Cambridge University Press, Cambridge (1999)

    MATH  Google Scholar 

  23. Song, B., Chan, T.F.: A fast algorithm for level set based optimization, Tech. Rep. CAM 02-68, UCLA (2002)

    Google Scholar 

  24. Tai, X.-C., Chan, T.F.: A survey on multiple set methods with applications for identifying piecewise constant functions. Int. J. Num. Anal. and Mod. 1, 25–48 (2004)

    MATH  MathSciNet  Google Scholar 

  25. Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the mumford and shah model. Int. J. of Comp. Vis. 50, 271–293 (2002)

    Article  MATH  Google Scholar 

  26. Weickert, J., Kühne, G.: Fast methods for implicit active contour models. In: Geometric level set methods in imaging, vision, and graphics, pp. 43–57. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  27. Zhao, H.-K., Chan, T., Merriman, B., Osher, S.: A variational level set approach to multiphase motion. J. Comput. Phys. 127, 179–195 (1996)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lie, J., Lysaker, M., Tai, XC. (2005). Piecewise Constant Level Set Methods and Image Segmentation. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds) Scale Space and PDE Methods in Computer Vision. Scale-Space 2005. Lecture Notes in Computer Science, vol 3459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408031_49

Download citation

  • DOI: https://doi.org/10.1007/11408031_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25547-5

  • Online ISBN: 978-3-540-32012-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics