Seeded Segmentation Methods for Medical Image Analysis

  • Camille Couprie
  • Laurent Najman
  • Hugues TalbotEmail author
Part of the Biological and Medical Physics, Biomedical Engineering book series (BIOMEDICAL)


Segmentation is one of the key tools in medical image analysis. The objective of segmentation is to provide reliable, fast, and effective organ delineation. While traditionally, particularly in computer vision, segmentation is seen as an early vision tool used for subsequent recognition, in medical imaging the opposite is often true. Recognition can be performed interactively by clinicians or automatically using robust techniques, while the objective of segmentation is to precisely delineate contours and surfaces. This can lead to effective techniques known as “intelligent scissors” in 2D and their equivalent in 3D.


Segmentation Method Edge Weight Active Contour Markov Random Field Segmentation Procedure 
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.
    Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 641–647 (1994)CrossRefGoogle Scholar
  2. 2.
    Appleton, B.: Globally minimal contours and surfaces for image segmentation. Ph.D. thesis, University of Queensland (2004). Http://
  3. 3.
    Appleton, B., Sun, C.: Circular shortest paths by branch and bound. Pattern Recognit. 36(11), 2513–2520 (2003)CrossRefGoogle Scholar
  4. 4.
    Appleton, B., Talbot, H.: Globally optimal geodesic active contours. J. Math. Imaging Vis. 23, 67–86 (2005)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ardon, R., Cohen, L.: Fast constrained surface extraction by minimal paths. Int. J. Comput. Vis. 69(1), 127–136 (2006)CrossRefGoogle Scholar
  6. 6.
    Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection. In: International Workshop on Image Processing. CCETT/IRISA, Rennes, France (1979)Google Scholar
  7. 7.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. PAMI 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  8. 8.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  9. 9.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  10. 10.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)zbMATHCrossRefGoogle Scholar
  11. 11.
    Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imaging Vis. 20(1–2), 89–97 (2004)MathSciNetGoogle Scholar
  12. 12.
    Chan, T., Bresson, X.: Continuous convex relaxation methods for image processing. In: Proceedings of ICIP 2010 (2010). Keynote talk,
  13. 13.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)zbMATHCrossRefGoogle Scholar
  14. 14.
    Cohen, L.D., Kimmel, R.: Global minimum for active contour models: A minimal path approach. Int. J. Comput. Vis. 24(1), 57–78 (1997). URL
  15. 15.
    Couprie, C., Grady, L., Najman, L., Talbot, H.: Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest. In: Proceedings of ICCV 2009, pp. 731–738. IEEE, Kyoto, Japan (2009)Google Scholar
  16. 16.
    Couprie, C., Grady, L., Najman, L., Talbot, H.: Power watersheds: A unifying graph-based optimization framework. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(7), 1384–1399 (2011)CrossRefGoogle Scholar
  17. 17.
    Couprie, C., Grady, L., Talbot, H., Najman, L.: Anisotropic diffusion using power watersheds. In: Proceedings of the International Conference on Image Processing (ICIP), pp. 4153–4156. Honk-Kong (2010)Google Scholar
  18. 18.
    Couprie, C., Grady, L., Talbot, H., Najman, L.: Combinatorial continuous maximum flows. SIAM J. Imaging Sci. (2010). URL In revision
  19. 19.
    Cousty, J., Bertrand, G., Najman, L., Couprie, M.: Watershed cuts: Minimum spanning forests and the drop of water principle. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1362–1374. (2008)Google Scholar
  20. 20.
    Cousty, J., Bertrand, G., Najman, L., Couprie, M.: Watershed cuts: Thinnings, shortest-path forests and topological watersheds. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 925–939 (2010)CrossRefGoogle Scholar
  21. 21.
    Cserti, J.: Application of the lattice Green’s function for calculating the resistance of an infinite network of resistors. Am. J. Phys. 68, 896 (2000)CrossRefGoogle Scholar
  22. 22.
    Daragon, X., Couprie, M., Bertrand, G.: Marching chains algorithm for Alexandroff-Khalimsky spaces. In: SPIE Vision Geometry XI, vol. 4794, pp. 51–62 (2002)Google Scholar
  23. 23.
    Dougherty, E., Lotufo, R.: Hands-on Morphological Image Processing. SPIE press, Bellingham (2003)CrossRefGoogle Scholar
  24. 24.
    Doyle, P., Snell, J.: Random Walks and Electric Networks. Carus Mathematical Monographs, vol. 22, p. 52. Mathematical Association of America, Washington, DC (1984)Google Scholar
  25. 25.
    Ford, J.L.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press, Princeton, NJ (1962)zbMATHGoogle Scholar
  26. 26.
    Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. PAMI 6, 721–741 (1984)zbMATHCrossRefGoogle Scholar
  27. 27.
    Goldberg, A., Tarjan, R.: A new approach to the maximum-flow problem. J. ACM 35, 921–940 (1988)MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    Goldenberg, R., Kimmel, R., Rivlin, E., Rudzsky, M.: Fast geodesic active contours. IEEE Trans. Image Process. 10(10), 1467–1475 (2001)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Grady, L.: Multilabel random walker image segmentation using prior models. In: Computer Vision and Pattern Recognition, IEEE Computer Society Conference, vol. 1, pp. 763–770 (2005). DOI
  30. 30.
    Grady, L.: Computing exact discrete minimal surfaces: Extending and solving the shortest path problem in 3D with application to segmentation. In: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference, vol. 1, pp. 69–78. IEEE (2006)Google Scholar
  31. 31.
    Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  32. 32.
    Grady, L., Funka-Lea, G.: Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials. In: Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, pp. 230–245. (2004)Google Scholar
  33. 33.
    Grady, L., Polimeni, J.: Discrete Calculus: Applied Analysis on Graphs for Computational Science. Springer Publishing Company, Incorporated, New York (2010)zbMATHGoogle Scholar
  34. 34.
    Grady, L., Schwartz, E.: Isoperimetric graph partitioning for image segmentation. Pattern Anal. Mach. Intell. IEEE Trans. 28(3), 469–475 (2006)CrossRefGoogle Scholar
  35. 35.
    Guigues, L., Cocquerez, J., Le Men, H.: Scale-sets image analysis. Int. J. Comput. Vis. 68(3), 289–317 (2006)CrossRefGoogle Scholar
  36. 36.
    Horowitz, S., Pavlidis, T.: Picture segmentation by a directed split-and-merge procedure. In: Proceedings of the Second International Joint Conference on Pattern Recognition, vol. 424, p. 433 (1974)Google Scholar
  37. 37.
    Iri, M.: Survey of Mathematical Programming. North-Holland, Amsterdam (1979)Google Scholar
  38. 38.
    Kakutani, S.: Markov processes and the Dirichlet problem. In: Proceedings of the Japan Academy, vol. 21, pp. 227–233 (1945)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comput. Vis. 1, 321–331 (1988)CrossRefGoogle Scholar
  40. 40.
    Khalimsky, E., Kopperman, R., Meyer, P.: Computer graphics and connected topologies on finite ordered sets. Topol. Appl. 36(1), 1–17 (1990)MathSciNetzbMATHCrossRefGoogle Scholar
  41. 41.
    Kruskal, J.J.: On the shortest spanning subtree of a graph and the travelling salesman problem. Proc. AMS 7(1) (1956)Google Scholar
  42. 42.
    Levinshtein, A., Stere, A., Kutulakos, K., Fleet, D., Dickinson, S., Siddiqi, K.: Turbopixels: Fast superpixels using geometric flows. Pattern Anal. Mach. Intell. IEEE Trans. 31(12), 2290–2297 (2009)CrossRefGoogle Scholar
  43. 43.
    Malladi, R., Sethian, J., Vemuri, B.: Shape modelling with front propagation: A level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 158–175 (1995)CrossRefGoogle Scholar
  44. 44.
    Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. Ser. B Biol. Sci. 207, 187–217 (1980)CrossRefGoogle Scholar
  45. 45.
    Menzies, S.W., Crotty, K.A., Ingvar, C., McCarthy, W.H.: An Atlas of Surface Microscopy of Pigmented Skin Lesions. McGraw-Hill, Roseville, Australia (1996). ISBN 0 07 470206 8Google Scholar
  46. 46.
    Meyer, F.: Topographic distance and watershed lines. Signal Process. 38(1), 113–125 (1994)zbMATHCrossRefGoogle Scholar
  47. 47.
    Meyer, F., Beucher, S.: Morphological segmentation. J. Vis. Commun. Image Represent. 1(1), 21–46 (1990)CrossRefGoogle Scholar
  48. 48.
    Mortensen, E., Barrett, W.: Interactive segmentation with intelligent scissors. Graph. Models Image Process. 60(5), 349–384 (1998)zbMATHCrossRefGoogle Scholar
  49. 49.
    Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42(5), 577–685 (1989)MathSciNetzbMATHCrossRefGoogle Scholar
  50. 50.
    Najman, L.: On the equivalence between hierarchical segmentations and ultrametric watersheds. Journal of Mathematical Imaging and Vision, 40(3), 231–247 (2011)MathSciNetCrossRefGoogle Scholar
  51. 51.
    Najman, L., Schmitt, M.: Geodesic saliency of watershed contours and hierarchical segmentation. Pattern Anal. Mach. Intell. IEEE Trans. 18(12), 1163–1173 (2002)CrossRefGoogle Scholar
  52. 52.
    Najman, L., Talbot, H. (eds.): Mathematical Morphology: From theory to applications. ISTE-Wiley, London, UK (2010)zbMATHGoogle Scholar
  53. 53.
    Osher, S., Sethian, J.: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)MathSciNetzbMATHCrossRefGoogle Scholar
  54. 54.
    Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11, 285–296 (1975)CrossRefGoogle Scholar
  55. 55.
    Paragios, N., Deriche, R.: Geodesic active contours and level sets for the detection and tracking of moving objects. Pattern Anal. Mach. Intell. IEEE Trans. 22(3), 266–280 (2002)CrossRefGoogle Scholar
  56. 56.
    Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. Int. J. Comput. Vis. 46(3), 223–247 (2002)zbMATHCrossRefGoogle Scholar
  57. 57.
    Pham, D., Xu, C., Prince, J.: Current methods in medical image segmentation1. Biomed. Eng. 2(1), 315 (2000)Google Scholar
  58. 58.
    Pock, T., Cremers, D., Bischof, H., Chambolle, A.: An algorithm for minimizing the Mumford-Shah functional. In: 12th International Conference on Computer Vision, pp. 1133–1140. IEEE (2009)Google Scholar
  59. 59.
    Prim, R.: Shortest connection networks and some generalizations. Bell Syst. Techn. J. 36(6), 1389–1401 (1957)Google Scholar
  60. 60.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D 60(1-4), 259–268 (1992). DOI Google Scholar
  61. 61.
    Sagiv, C., Sochen, N., Zeevi, Y.: Integrated active contours for texture segmentation. Image Process. IEEE Trans. 15(6), 1633–1646 (2006)CrossRefGoogle Scholar
  62. 62.
    Sethian, J.: Level set methods and fast marching methods. Cambridge University Press, Cambridge (1999). ISBN 0-521-64204-3zbMATHGoogle Scholar
  63. 63.
    Soille, P.: Constrained connectivity for hierarchical image partitioning and simplification. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1132–1145 (2008)CrossRefGoogle Scholar
  64. 64.
    Stawiaski, J., Decencière, E., Bidault, F.: Computing approximate geodesics and minimal surfaces using watershed and graph cuts. In: Banon, G.J.F., Barrera, J., Braga-Neto, U.d.M., Hirata, N.S.T. (eds.) Proceedings of the 8th International Symposium on Mathematical Morphology, vol. 1, pp. 349–360. Instituto Nacional de Pesquisas Espaciais (INPE) (2007). URL
  65. 65.
    Strang, G.: Maximal flow through a domain. Math. Program. 26, 123–143 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  66. 66.
    Veksler, O.: Efficient graph-based energy minimization. Ph.D. thesis, Cornell University (1999)Google Scholar
  67. 67.
    Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991)CrossRefGoogle Scholar
  68. 68.
    Weickert, J., Romeny, B., Viergever, M.: Efficient and reliable schemes for nonlinear diffusion filtering. Image Process. IEEE Trans. 7(3), 398–410 (2002)CrossRefGoogle Scholar
  69. 69.
    Zhu, S., Yuille, A.: Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. Pattern Anal. Mach. Intell. IEEE Trans. 18(9), 884–900 (2002)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Camille Couprie
    • 1
  • Laurent Najman
    • 1
  • Hugues Talbot
    • 1
    Email author
  1. 1.Université Paris-EstParisFrance

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