Multiscale Graph-Cut for 3D Segmentation of Compact Objects

  • Miroslav JirikEmail author
  • Vladimir Lukes
  • Milos Zelezny
  • Vaclav Liska
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11255)


The article is a step forward towards improving image segmentation using a popular method called Graph-Cut. We focus on optimizing the algorithm for processing data, in which the target object occupies only a small portion of the total volume. We propose a two-step procedure. At the first step, the location of the object is determined roughly. At the second step, Graph-Cut segmentation is performed with a special multi-scale chart structure. Two different graph construction methods are suggested. The calculation time of both variants is compared with the original Graph-Cut method. The msgc_lo2hi method has been shown to provide a statistically significant time reduction of the computational costs.


Graph-Cut method Multiscale Medical imaging Image segmentation 



This work has been supported by Charles University Research Centre program UNCE/MED/006 “University Center of Clinical and Experimental Liver Surgery” and Ministry of Education project ITI CZ.02.1.01/0.0/0.0/17_048/0007280: Application of modern technologies in medicine and industry and Erasmus+ project MedTrain3DModsim, nr. 2016-1-TR01-KA203-034929 provided by the Turkish National Agency. The research is also supported by the project LO 1506 of the Czech Ministry of Education, Youth and Sports. The authors appreciate the access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the program “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042).


  1. 1.
    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
  2. 2.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)CrossRefGoogle Scholar
  3. 3.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 359–374 (2001)zbMATHGoogle Scholar
  4. 4.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Eberlova, L., et al.: Porcine liver vascular bed in Biodur E20 corrosion casts. Folia Morphologica 75(2), 154–161 (2016)CrossRefGoogle Scholar
  6. 6.
    Gotra, A., et al.: Liver segmentation: indications, techniques and future directions. Insights Imaging 8(4), 377–392 (2017)CrossRefGoogle Scholar
  7. 7.
    Grieg, D.M., Porteous, B.T., Scheult, A.H.: Exact maximum a posteriori estimation for binary images. J. R. Stat. Soc. 51(2), 271–279 (1989)Google Scholar
  8. 8.
    Jirik, M., Lukes, V.: imcut - 3D multiscale Graph-Cut segmentation module for python (2018).
  9. 9.
    Jirik, M., Lukeš, V.: LISA - Liver Surgery Analyser.
  10. 10.
    Kang, S.M., Wan, J.W.L.: A multiscale graph cut approach to bright-field multiple cell image segmentation using a Bhattacharyya measure. In: Proceedings of SPIE 8669, Medical Imaging 2013: Image Processing, p. 86693S (2013)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Biomedical Center, Faculty of Medicine in PilsenCharles UniversityPilsenCzech Republic
  2. 2.Faculty of Applied SciencesNTIS - New Technologies for the Information SocietyPilsenCzech Republic
  3. 3.Biomedical Center and Department of Surgery, University Hospital and Faculty of Medicine in PilsenCharles UniversityPilsenCzech Republic

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