Journal of Real-Time Image Processing

, Volume 13, Issue 1, pp 121–133 | Cite as

Accelerated liver tumor segmentation in four-phase computed tomography images

  • Faten ChaiebEmail author
  • Tarek Ben Said
  • Sabra Mabrouk
  • Faouzi Ghorbel
Special Issue Paper


Segmentation and volume measurement of liver tumor are important tasks for surgical planning and cancer follow-up. In this work, a segmentation method from four-phase computed tomography images is proposed. It is based on the combination of the Expectation-Maximization algorithm and the Hidden Markov Random Fields. The latter considers the spatial information given by voxel neighbors of two contrast phases. The segmentation algorithm is applied on a volume of interest that decreases the number of processed voxels. To accelerate the classification steps within the segmentation process, a Bootstrap resampling scheme is also adopted. It consists in selecting randomly an optimal representative set of voxels. The experimental results carried out on three clinical datasets show the performance of our liver tumor segmentation method. It has been notably observed that the computing time of the classification algorithm is reduced without any significant impact on the segmentation accuracy.


Segmentation Liver tumor HMRF-EM Bootstrap resampling Computed tomography 



The authors thank Dr. Olfa Azaiz and Prof. Emna Mnif from the Department of Radiology, La Rabta Hospital, Tunis, Tunisia.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

No need (no personal data are used in this work).


  1. 1.
    Stawiaski, J., Decencire, E., Bidault, F.: Interactive liver tumor segmentation using graph cuts and watershed. In: Workshop on 3D Segmentation in the Clinic: A Grand Challenge II. Liver Tumor Segmentation Challenge. MICCAI (2008).
  2. 2.
    Lu, R., Marziliano, P., Thng, C.: Liver tumor volume estimation by semi-automatic segmentation method. In: Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 3296–3299 (2005). doi: 10.1109/IEMBS.2005.1617181
  3. 3.
    Yim, P., Foran, D.: Volumetry of hepatic metastases in computed tomography using the watershed and active contour algorithms. In: Computer-Based Medical Systems, 2003. Proceedings. 16th IEEE Symposium, pp. 329–335 (2003). doi: 10.1109/CBMS.2003.1212810
  4. 4.
    Li, B.N., Chui, C.K., Chang, S., Ong, S.H.: A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images. Expert Syst. Appl. 39(10), 9661–9668 (2012). doi: 10.1016/j.eswa.2012.02.095 CrossRefGoogle Scholar
  5. 5.
    Smeets, D., Loeckx, D., Stijnen, B., Dobbelaer, B.D., Vandermeulen, D., Suetens, P.: Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification. Med. Image Anal. 14(1), 13–20 (2010). doi: 10.1016/ CrossRefGoogle Scholar
  6. 6.
    Wong, D., Liu, J., Yin, F., Tian, Q., Xiong, W., Zhou, J., Yingyi, Q., Han, T., Venkatesh, S., Wang, S.: A semi-automated method for liver tumor segmentation based on 2D region growing with knowledge-based constraints. In: Medical Image Computing and Computer Assisted Intervention (2008).
  7. 7.
    Zhou, J.-Y., Wong, D.W.K., Ding, F., Venkatesh, S.K., Tian, Q., Qi, Y.-Y., Xiong, W., Liu, J.J., Leow, W.-K.: Liver tumour segmentation using contrast-enhanced multi-detector CT data: performance benchmarking of three semiautomated methods. Comput. Appl. Eur. Radiol. 20(7), 1738–1748 (2010)CrossRefGoogle Scholar
  8. 8.
    Freiman, M., Eliassaf, O., Taieb, Y., Joskowicz, L., Azraq, Y., Sosna, J.: An iterative Bayesian approach for nearly automatic liver segmentation: algorithm and validation. Int. J. Comput. Assist. Radiol. Surg. 3(5), 439–446 (2008). doi: 10.1007/s11548-008-0254-1 CrossRefGoogle Scholar
  9. 9.
    Zhou, J., Xiong, W., Tian, Q., Qi, Y., Liu, J., Leow, W.K., Han, T., Venkatesh, S.K., Wang, S.: Semi-automatic segmentation of 3D liver tumors from CT scans using voxel classification and propagational learning. In: Workshop on 3D Segmentation in the Clinic: A Grand Challenge II Liver Tumor Segmentation Challenge (2008).
  10. 10.
    Moltz, J., Bornemann, L., Dicken, V., Peitgen, H.: Segmentation of liver metastases in CT scans by adaptive thresholding and morphological processing. In: Workshop on 3D Segmentation in the Clinic: A Grand Challenge II Liver Tumor Segmentation Challenge (2008).
  11. 11.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001). doi: 10.1109/42.906424 CrossRefGoogle Scholar
  12. 12.
    Pieczynski, W.: Modèles de Markov en traitements d’images. Traitement du Signal 20(3), 255–278 (2003).
  13. 13.
    Besag, J.: On the statistical analysis of dirty pictures. J. Roy. Stat. Soc. Lond. B. 48(3), 259–302 (1986). doi: 10.2307/2345426
  14. 14.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39(1), 1–38 (1977). doi: 10.2307/2984875.
  15. 15.
    M’hiri, S., Cammoun, L., Ghorbel, F.: Speeding up HMRF-EM algorithms for fast unsupervised image segmentation by bootstrap resampling: application to the brain tissue segmentation. Signal Process. 87(11), 2544–2559 (2007). doi: 10.1016/j.sigpro.2007.04.010
  16. 16.
    Mhiri, S., Mabrouk, S., Ghorbel, F.: Segmentation des IRM cérébrales par une variante bootstrapée du HMRF-EM: étude préliminaire sur fantômes. IRBM 33(1), 2–10 (2012). doi: 10.1016/j.irbm.2011.12.005, numro spcial TAIMA 2012
  17. 17.
    Efron, B.: Bootstrap methods: another look at the jackknife. Ann. Stat. 7(1), 1–26 (1979). doi: 10.1214/aos/1176344552 MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Efron, B., Tibshirani, R.: Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1(1), 54–75 (1986). doi: 10.1214/ss/1177013815 MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Ghorbel, F., Banga, C.: Bootstrap sampling applied to image analysis. Int. Conf. Acoust. Speech Signal Process. 5, 81–84 (1994). doi: 10.1109/ICASSP.1994.389932 Google Scholar
  20. 20.
    Lu, H., Li, X., Hsiao, T., Liang, Z.: Analytical noise treatment for low-dose CT projection data by penalized weighted least-square smoothing in the K-L domain. In: Proc. SPIE 4682, Medical Imaging 2002: Physics of Medical Imaging, pp. 146–152 (May 2, 2002). doi: 10.1117/12.465552
  21. 21.
    Gravel, P., Beaudoin, G., De Guise, J., et al.: A method for modeling noise in medical images. IEEE Trans. Med. Imaging 23(10), 1221–1232 (2004)CrossRefGoogle Scholar
  22. 22.
    Lei, T., Sewchand, W.: Statistical approach to X-ray CT imaging and its applications in image analysis. I. Statistical analysis of X-ray CT imaging. IEEE Trans. Med. Imaging 11(1), 53–61 (1992)CrossRefGoogle Scholar
  23. 23.
    Deng, X., Du, G.: Editorial. In: MICCAI Workshop Proceedings of 3D Segmentation in the Clinic: A Grand Challenge II—Liver Tumor Segmentation (2008)Google Scholar
  24. 24.
    Smeets, D., Stijnen, B., Loeckx, D., Dobbelaer, B.D., Suetens, P.: Segmentation of liver metastases using a level set method with spiral-scanning technique and supervised fuzzy pixel classification. In: Workshop Proceedings of the 11th International Conference on Medical Image Computing and Computer Assisted Intervention—MICCAI (2008).

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Faten Chaieb
    • 1
    Email author
  • Tarek Ben Said
    • 1
  • Sabra Mabrouk
    • 1
  • Faouzi Ghorbel
    • 1
  1. 1.CRISTAL Laboratory, ENSIUniversity of ManoubaManoubaTunisia

Personalised recommendations