Collaborative Multi Organ Segmentation by Integrating Deformable and Graphical Models

  • Mustafa Gökhan Uzunbaş
  • Chao Chen
  • Shaoting Zhang
  • Kilian M. Pohl
  • Kang Li
  • Dimitris Metaxas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)


Organ segmentation is a challenging problem on which significant progress has been made. Deformable models (DM) and graphical models (GM) are two important categories of optimization based image segmentation methods. Efforts have been made on integrating two types of models into one framework. However, previous methods are not designed for segmenting multiple organs simultaneously and accurately. In this paper, we propose a hybrid multi organ segmentation approach by integrating DM and GM in a coupled optimization framework. Specifically, we show that region-based deformable models can be integrated with Markov Random Fields (MRF), such that multiple models’ evolutions are driven by a maximum a posteriori (MAP) inference. It brings global and local deformation constraints into a unified framework for simultaneous segmentation of multiple objects in an image. We validate this proposed method on two challenging problems of multi organ segmentation, and the results are promising.


Graphical Model Right Ventricle Markov Random Fields Right Atrium Deformable 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.


  1. 1.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. J. Computer Vision 70(2), 109–131 (2006)CrossRefGoogle Scholar
  2. 2.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. on PAMI 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  3. 3.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Computer Vision 22(1), 61–79 (1997)CrossRefzbMATHGoogle Scholar
  4. 4.
    Chen, X., Udupa, J.K., Bagci, U., Zhuge, Y., Yao, J.: Medical image segmentation by combining graph cuts and oriented active appearance models. IEEE Trans. Image Processing 21(4), 2035–2046 (2012)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Cohen, L.D., Cohen, I.: Finite element methods for active contour models and balloons for 2d and 3d images. IEEE Trans. on PAMI 15, 1131–1147 (1991)CrossRefGoogle Scholar
  6. 6.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. PAMI 23(6), 681–685 (2001)CrossRefGoogle Scholar
  7. 7.
    Huang, R., Pavlovic, V., Metaxas, D.: A graphical model framework for coupling MRFs and deformable models. In: CVPR. vol. 2, pp. II-739–II-746 (2004)Google Scholar
  8. 8.
    Huang, X., Metaxas, D., Chen, T.: Metamorphs: Deformable shape and texture models. In: CVPR, vol. 1, pp. 496–503 (2004)Google Scholar
  9. 9.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Computer Vision. 1, 321–331 (1988)CrossRefGoogle Scholar
  10. 10.
    Kohlberger, T., Uzunbaş, M.G., Alvino, C., Kadir, T., Slosman, D.O., Funka-Lea, G.: Organ segmentation with level sets using local shape and appearance priors. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 34–42. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? IEEE Trans. on PAMI 26(2), 147–159 (2004)CrossRefGoogle Scholar
  12. 12.
    Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the mumford and shah model. Int. J. of Computer Vision 50, 271–293 (2001)CrossRefGoogle Scholar
  13. 13.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain mr images through a hidden markov random field model and the expectation-maximization algorithm. TMI 20(1), 45–57 (2001)Google Scholar
  14. 14.
    Zhu, S.C., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Trans. on PAMI 18(9), 884–900 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mustafa Gökhan Uzunbaş
    • 1
  • Chao Chen
    • 1
  • Shaoting Zhang
    • 1
  • Kilian M. Pohl
    • 2
  • Kang Li
    • 3
  • Dimitris Metaxas
    • 1
  1. 1.CBIMRutgers UniversityPiscatawayUSA
  2. 2.University of PennsylvaniaPhiladelphiaUSA
  3. 3.Dept. of Industrial and Systems EngineeringRutgers UniversityPiscatawayUSA

Personalised recommendations