Advertisement

A Hybrid Model for Liver Shape Segmentation with Customized Fast Marching and Improved GMM-EM

  • Weizhuo Huang
  • Yinwei ZhanEmail author
  • Rongqian Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11902)

Abstract

This paper describes an approach to segment liver shape from abdominal CT sequences, required by the analysis of liver diseases. A rough segmentation is first conducted via a customized Fast Marching method to obtain an approximate 3D liver region for subsequent procedure. Then, an improvement of GMM-EM algorithm is made to extract the accurate liver region. Experimental results, evaluated on non-tumor series and tumor series of 10 cases, show that the proposed method performs better than several other typical segmentation models in running time and precision.

Keywords

Liver segmentation Fast Marching GMM-EM K-means++ 

Notes

Acknowledgement

This work is supported by the Science and Technology Planning Project of Guangdong Province with grant numbers 2017B010110007 and 2017B010110015.

References

  1. 1.
    Farzaneh, N., Habbo-Gavin, S., Soroushmehr, S.M.R., Patel, H., Fessell, D.P., Ward, K.R., et al.: Atlas based 3D liver segmentation using adaptive thresholding and superpixel approaches. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1093–1097. IEEE, New Orleans (2017)Google Scholar
  2. 2.
    Lu, F., Wu, F., Hu, P., Peng, Z., Kong, D.: Automatic 3d liver location and segmentation via convolutional neural network and graph cut. Int. J. Comput. Assist. Radiol. Surg. 12(2), 171–182 (2017)CrossRefGoogle Scholar
  3. 3.
    Chen, Y., Wang, Z., Hu, J., Zhao, W.: The domain knowledge based graph-cut model for liver CT segmentation. Biomed. Signal Process. Control 7, 591–598 (2012)CrossRefGoogle Scholar
  4. 4.
    Huang, L., Weng, M., Shuai, H., Huang, Y., Sun, J., Gao, F.: Automatic liver segmentation from CT images using single-block linear detection. Biomed. Res. Int. 2016, 1–11 (2016). HindawiGoogle Scholar
  5. 5.
    Yang, X., et al.: Segmentation of liver and vessels from CT images and classification of liver segments for preoperative liver surgical planning in living donor liver transplantation. Comput. Meth. Programs Biomed. 158, 41–52 (2018)CrossRefGoogle Scholar
  6. 6.
    Xia, Y., Ji, Z., Zhang, Y.: Brain MRI image segmentation based on learning local variational Gaussian mixture models. Neurocomputing 204, 189–197 (2016)CrossRefGoogle Scholar
  7. 7.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton Jacobi formulations. J. Comput. Phys. 79, 12–49 (1988)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. Proc. Natl. Acad. Sci. 93(4), 1591–1595 (1996)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Ho, H., Bier, P., Sands, G., Hunter, P.: Cerebral artery segmentation with level set methods. In: Proceedings of Image and Vision Computing New Zealand, pp. 300–304. Hamilton, New Zealand, December 2007Google Scholar
  10. 10.
    Yan, J., Zhuang, T.: Applying improved fast marching method to endocardial boundary detection in echocardiographic images. Pattern Recogn. Lett. 24(15), 2777–2784 (2003)CrossRefGoogle Scholar
  11. 11.
    Campadelli, P., Casiraghi, E., Pratissoli, S.: Fully automatic segmentation of abdominal organs from CT images using fast marching methods. In: 21st IEEE International Symposium on Computer-Based Medical Systems, pp. 1–5. IEEE, Jyvaskyla, Finland (2008)Google Scholar
  12. 12.
    Lee, J., et al.: Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images. Comput. Methods Programs Biomed. 88, 26–38 (2007)CrossRefGoogle Scholar
  13. 13.
    Ibáñez, L., et al.: The ITK Software Guide. 2nd ed., Kitware, Inc., Clifton Park (2005)Google Scholar
  14. 14.
    Yang, X., Yu, H.C., Choi, Y., Lee, W., Wang, B., Yang, J., et al.: A hybrid semi-automatic method for liver segmentation based on levelset methods using multiple seed points. Comput. Methods Programs Biomed. 113, 69–79 (2014)CrossRefGoogle Scholar
  15. 15.
    Ali, H., Elmogy, M., El-Daydamony, E., Atwan, A.: Multi-resolution MRI brain image segmentation based on morphological pyramid and fuzzy c-mean clustering. Arabian J. Sci. Eng. 40(11), 3173–3185 (2015)CrossRefGoogle Scholar
  16. 16.
    Campadelli, P., Casiraghi, E., Esposito, A.: Liver segmentation from computed tomography scans: a survey and a new algorithm. Artif. Intell. Med. 45(2–3), 185–196 (2009)CrossRefGoogle Scholar
  17. 17.
    Gómez, J.V., Álvarez, D., Garrido, S., Moreno, L.: Fast Methods for Eikonal equations: an experimental survey. IEEE Access 7, 39005–39029 (2019)CrossRefGoogle Scholar
  18. 18.
    Capozzoli, A., Curcio, C., Liseno, A., Savarese, S.: A comparison of Fast Marching, Fast Sweeping and Fast Iterative Methods for the solution of the eikonal equation. In: 21st Telecommunications Forum Telfor (TELFOR), pp. 685–688. IEEE, Belgrade (2013)Google Scholar
  19. 19.
    Breuß, M., Cristiani, E., Gwosdek, P., Vogel, O.: An adaptive domain-decomposition technique for parallelization of the fast marching method. Appl. Math. Comput. 218(1), 32–44 (2011)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Forcadel, N., Guyader, C.L., Gout, C.: Generalized fast marching method: applications to image segmentation. Numer. Algorithms 48, 189–211 (2008)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Baghdadi, M., Benamrane, N., Sais, L.: Fuzzy generalized fast marching method for 3d segmentation of brain structures. Int. J. Imaging Syst. Technol. 27(3), 281–306 (2017)CrossRefGoogle Scholar
  22. 22.
    Ascoli, Giorgio A., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds.): BIH 2016. LNCS (LNAI), vol. 9919, pp. 52–60. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47103-7CrossRefGoogle Scholar
  23. 23.
    Portela, N.M., Cavalcanti, G.D.C., Ren, T.I.: Semi-supervised clustering for MR brain image segmentation. Expert Syst. Appl. 41(4), 1492–1497 (2014)CrossRefGoogle Scholar
  24. 24.
    Kapoor, A., Singhal, A.: A comparative study of K-Means, K-Means++ and Fuzzy C-Means clustering algorithms. In: 3rd International Conference on Computational Intelligence & Communication Technology (CICT), pp. 1–6. IEEE, Palo Alto (2017)Google Scholar
  25. 25.
    Singh, I.: Segmentation of liver using hybrid K-means clustering and level set. Int. J. Adv. Res. Comput. Sci. Software Eng. 5(8), 742–746 (2015)Google Scholar
  26. 26.
    Singh, P., Khanna, V., Kamal, M.: Hemorrhage segmentation by fuzzy c-mean with Modified Level Set on CT imaging. In: 5th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 550–555. IEEE, Noida (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of ComputerGuangdong University of TechnologyGuangzhouChina
  2. 2.School of Materials Science and EngineeringSouth China University of TechnologyGuangzhouChina

Personalised recommendations