Advertisement

Liver Segmentation Approach Using Graph Cuts and Iteratively Estimated Shape and Intensity Constrains

  • Ahmed Afifi
  • Toshiya Nakaguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7511)

Abstract

In this paper, we present a liver segmentation approach. In which, the relation between neighboring slices in CT images is utilized to estimate shape and statistical information of the liver. This information is then integrated with the graph cuts algorithm to segment the liver in each CT slice. This approach does not require prior models construction, and it uses single phase CT images; even so, it is talented to deal with complex shape and intensity variations. Moreover, it eliminates the burdens associated with model construction like data collection, manual segmentation, registration, and landmark correspondence. In contrast, it requires a low user interaction to determine the liver landmarks on a single CT slice only. The proposed approach has been evaluated on 10 CT images with several liver abnormalities, including tumors and cysts, and it achieved high average scores of 81.7 using MICCAI-2007 Grand Challenge scoring system. Compared to contemporary approaches, our approach requires significantly less interaction and processing time.

Keywords

Statistical Shape Model Liver Object Liver Segmentation Current Slice Liver Shape 
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.

References

  1. 1.
    Campadelli, P., Casiraghi, E., Esposito, A.: Liver segmentation from computed tomography scans: A survey and a new algorithm. Artificial Intelligence in Medicine 45, 185–196 (2009)CrossRefGoogle Scholar
  2. 2.
    Heimann, T., Ginneken, B.V., Styner, M.A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., Bello, F., Binnig, G., Bischof, H., Bornik, A., Cashman, P.M.M., Chi, Y., Córdova, A., Dawant, B.M., Fidrich, M., Furst, J.D., Furukawa, D., Grenacher, L., Hornegger, J., Kainmüller, D., Kitney, R.I., Kobatake, H., Lamecker, H., Lange, T., Lee, J., Lennon, B., Li, R., Li, S., Meinzer, H.P., Németh, G., Raicu, D.S., Rau, A.M., van Rikxoort, E.M., Rousson, M., Ruskó, L., Saddi, K.A., Schmidt, G., Seghers, D., Shimizu, A., Slagmolen, P., Sorantin, E., Soza, G., Susomboon, R., Waite, J.M., Wimmer, A., Wolf, I.: Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets. IEEE Trans. Med. Imag. 28(8), 1251–1265 (2009)CrossRefGoogle Scholar
  3. 3.
    Rusko, L., Bekes, G., Fidrich, M.: Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images. Medical Image Analysis 13, 871–882 (2009)CrossRefGoogle Scholar
  4. 4.
    Foruzana, A.H., Zoroofia, R.A., Horib, M., Satoc, Y.: Liver segmentation by intensity analysis and anatomical information in multi-slice CT images. International Journal of CARS 4(3), 287–297 (2009)CrossRefGoogle Scholar
  5. 5.
    Tibamoso, G., Rueda, A.: Semi-automatic Liver Segmentation From Computed Tomography (CT) Scans based on Deformable Surfaces. SLIVER07 Results (October 2009), http://sliver07.isi.uu.nl/results/20091022201318/description.pdf
  6. 6.
    Gao, J., Kosaka, A., Kak, A.: A Deformable Model for Automatic CT Liver Extraction. Academic Radiology 12(9), 1178–1189 (2005)CrossRefGoogle Scholar
  7. 7.
    Xu, C., Prince, J.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Alomari, R.S., Kompalli, S., Chaudhary, V.: Segmentation of the Liver from Abdominal CT Using Markov Random Field model and GVF Snakes. In: Proc. International Conference on Complex, Intelligent and Software Intensive Systems, pp. 293–298 (2008)Google Scholar
  9. 9.
    Sethian, J.A.: Level Set Methods and Fast Marching Methods, 2nd edn., pp. 1–74. Cambridge University Press (1996)Google Scholar
  10. 10.
    Kainmuller, D., Lange, T., Lamecker, H.: Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model. In: Proc. MICCAI Workshop on 3-D Segmentation in Clinic: A grand Challenge, pp. 109–116 (2007)Google Scholar
  11. 11.
    Afifi, A., Nakaguchi, T., Tsumura, N., Miyake, Y.: A Model Optimization Approach to the Automatic Segmentation of Medical Images. IEICE Trans. on Information and Systems E93-D(4), 882–889 (2010)CrossRefGoogle Scholar
  12. 12.
    Linguraru, M.G., Pura, J.A., Chowdhury, A.S., Summers, R.M.: Multi-organ Segmentation from Multi-phase Abdominal CT via 4D Graphs Using Enhancement, Shape and Location Optimization. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 89–96. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Okada, T., Shimada, R., Hori, M., Nakamoto, M., Chen, Y.W., Nakamura, H., Sato, Y.: Automated Segmentation of the Liver from 3D CT Images Using Probabilistic Atlas and Multilevel Statistical Shape Model. Academic Radiology 15(11), 1390–1399 (2008)CrossRefGoogle Scholar
  14. 14.
    Lee, J., Kim, N., Lee, H., Seo, J.B., Won, H.J., Shin, Y.M., Shin, Y.G., Kim, S.-H.: Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images. Computer Methods and Programs in Biomedicine 88, 26–38 (2007)CrossRefGoogle Scholar
  15. 15.
    Weickert, J., Romeny, B.M., Viergever, M.A.: Efficient and Reliable Schemes for Nonlinear Diffusion Filtering. IEEE Trans. Image Process. 7(3), 398–410 (1998)CrossRefGoogle Scholar
  16. 16.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. on Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  17. 17.
    Heimann, T., Ginneken, B.V., Styner, M.A.: Segmentation of the Liver 2007 (SLIVER07), http://sliver07.isi.uu.nl/ (last visted: June 10, 2012)

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ahmed Afifi
    • 1
  • Toshiya Nakaguchi
    • 2
  1. 1.Faculty of Computers and InformationMenoufia UniversityEgypt
  2. 2.Graduate School of EngineeringChiba UniversityJapan

Personalised recommendations