Advertisement

Segmentation of Brain Tumors in CT Images Using Level Sets

  • Zhenwen Wei
  • Caiming Zhang
  • Xingqiang Yang
  • Xiaofeng Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7431)

Abstract

This paper proposes an approach based on level sets to segment brain tumors from CT images. Combining edge information with region information dynamically, the novel method introduces a new energy function model, which will make the initial contour evolve towards the desirable boundary while not leak at weak edge positions. In addition, re-initialization of the evolving level set function is avoided by introducing a new simple regularization term, which can eliminate radical changes of level set function(LSF) far away from the contour, and make the LSF prone to be a signed distance function around the contour as well. Experimental results demonstrate that the proposed method performs well on CT images, and can segment brain tumors exactly.

Keywords

Brain Tumor Active Contour Regularization Term Initial Contour Signed Distance Function 
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.
    Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. Springer (2002)Google Scholar
  2. 2.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22, 61–79 (1997)zbMATHCrossRefGoogle Scholar
  3. 3.
    Li, C., Xu, C., Gui, C., Fox, M.: Level set evolution without re-initialization: a new variational formulation. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 430–436 (2005)Google Scholar
  4. 4.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Li, C., Kao, C., Gore, J., Ding, Z.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17, 1940–1949 (2008)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Li, C., Xu, C., Gui, C., Fox, M.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19, 3243–3254 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chen, S., Radke, R.: Level set segmentation with both shape and intensity priors. In: Proc. IEEE Int. Conf. Comput. Vis., pp. 763–770 (2009)Google Scholar
  8. 8.
    Tsai, A., Yezzi Jr., A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W., Willsky, A.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imag. 22, 3243–3254 (2003)CrossRefGoogle Scholar
  9. 9.
    Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape. Int. J. Comput. Vis. 72, 195–215 (2003)CrossRefGoogle Scholar
  10. 10.
    Yu, Y., Zhang, C., Wei, Y., Li, X.: Active Contour Method Combining Local Fitting Energy and Global Fitting Energy Dynamically. In: Zhang, D., Sonka, M. (eds.) ICMB 2010. LNCS, vol. 6165, pp. 163–172. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhenwen Wei
    • 1
  • Caiming Zhang
    • 1
    • 2
  • Xingqiang Yang
    • 1
  • Xiaofeng Zhang
    • 1
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina
  2. 2.Shandong Province Key Lab. of Digital Media TechnologyShandong University of Finance and EconomicsJinanChina

Personalised recommendations