Level Set-Based Fast Multi-phase Graph Partitioning Active Contours Using Constant Memory

  • Filiz Bunyak
  • Kannappan Palaniappan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5807)


We present multi-phase FastGPAC that extends our dramatic improvement of memory requirements and computational complexity on two-class GPAC, into multi-class image segmentation. Graph partitioning active contours GPAC is a recently introduced approach that elegantly embeds the graph-based image segmentation problem within a continuous level set-based active contour paradigm. However, GPAC similar to many other graph-based approaches has quadratic memory requirements. For example, a 1024x1024 grayscale image requires over one terabyte of working memory. Approximations of GPAC reduce this complexity by trading off accuracy. Our FastGPAC approach implements an exact GPAC segmentation using constant memory requirement of few kilobytes and enables use of GPAC on high throughput and high resolution images. Extension to multi-phase enables segmention of multiple regions of interest with different appearances. We have successfully applied FastGPAC on different types of images, particularly on biomedical images of different modalities. Experiments on the various image types, natural, biomedical etc. show promising segmentation results with substantially reduced computational requirements.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bertelli, L.: GPAC implementation,
  2. 2.
    Bertelli, L., Sumengen, B., Manjunath, B., Gibou, F.: A variational framework for multiregion pairwise similarity-based image segmentation. IEEE Trans. on Patt. Anal. Mach. Intell. 30(8), 1400–1414 (2008)CrossRefGoogle Scholar
  3. 3.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. J. Comp. Vision 70(2), 109–131 (2006)CrossRefGoogle Scholar
  4. 4.
    Bunyak, F., Palaniappan, K.: Efficient segmentation using feature-based graph partitioning active contours. In: Int’l Conf. Computer Vision (September 2009)Google Scholar
  5. 5.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Proc. 10(2), 266–277 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Cohen, L.: On active contour models and balloons. Comput. Vis., Graphics, Image Processing 53, 211–218 (1991)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Dejnozkova, E., Dokladal, P.: Embedded real-time architecture for level-set-based active contours. EURASIP Journal on Applied Signal Processing 2005(17), 2788–2803 (2005)CrossRefzbMATHGoogle Scholar
  8. 8.
    Jacob, M., Blu, T., Unser, M.: Efficient energies and algorithms for parametric snakes. IEEE Trans. Image Process. 13(9), 1231–1244 (2004)CrossRefGoogle Scholar
  9. 9.
    Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Patt. Anal. Mach. Intell. 26(2), 147–159 (2004)CrossRefGoogle Scholar
  10. 10.
    Malcolm, J., Rathi, Y., Tannenbaum, A.: A graph cut approach to image segmentation in tensor space. In: IEEE Conf. Comp. Vision and Patt. Rec., pp. 1–8 (2007)Google Scholar
  11. 11.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l Conf. Computer Vision, July 2001, vol. 2, pp. 416–423 (2001)Google Scholar
  12. 12.
    Paragios, N., Deriche, R.: Geodesic active regions for motion estimation and tracking. In: Proc. Int. Conf. Computer Vision, Corfu, Greece, pp. 688–694 (1999)Google Scholar
  13. 13.
    Ronfard, R.: Region-based strategies for active contour models. Int. J. Comput. Vision 13, 229–251 (1994)CrossRefGoogle Scholar
  14. 14.
    Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D 60, 259–268 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Sethian, J.: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry. In: Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press, Cambridge (1999)Google Scholar
  16. 16.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRefGoogle Scholar
  17. 17.
    Sumengen, B., Manjunath, B.S.: Graph partitioning active contours (GPAC) for image segmentation. IEEE Trans. Patt. Anal. Mach. Intell., 509–521 (April 2006)Google Scholar
  18. 18.
    Vese, L., Chan, T.: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Computer Vision 50(3), 271–293 (2002)CrossRefzbMATHGoogle Scholar
  19. 19.
    Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 15(11), 1101–1113 (1993)CrossRefGoogle Scholar
  20. 20.
    Wang, X., He, W., Metaxas, D., Matthew, R., White, E.: Cell segmentation and tracking using texture-adaptive snakes. In: Proc. IEEE Int. Symp. Biomedical Imaging, Washington, DC, April 2007, pp. 101–104 (2007)Google Scholar
  21. 21.
    Zhu, S., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 18, 884–900 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Filiz Bunyak
    • 1
  • Kannappan Palaniappan
    • 1
  1. 1.Department of Computer ScienceUniversity of Missouri-ColumbiaColumbiaUSA

Personalised recommendations