Automatic Segmentation of the Liver in CT Images Using a Model of Approximate Contour

  • Marcin Ciecholewski
  • Krzysztof Dȩbski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4263)


The segmentation of the liver structure from a computed tomography (CT) image is an important function of the software designed to assist liver diagnostics, because it allows for the elimination of excess information from the diagnostic process. In this paper, the task of segmentation has been implemented through first finding the contour of the liver which is made up of a finite number of joint polylines approximating individual fragments of the liver boundary in the CT image. Next, the field outside the contour is divided into two polygons and eliminated from the image. The initial reference point for the calculations is the lumbar section of the spine which is a central point of any CT image of the liver. The automatic method of segmentation is to be used in a dedicated computer system designed to diagnose liver patients.


Compute Tomography Image Abdominal Cavity Automatic Segmentation Segmented Structure Liver Structure 
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.


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  1. 1.
    Bae, K.T., Giger, M.L., Chen, C.T., Kahn Jr., C.E.: Automatic segmentation of liver structure in CT images. Medical Physics 20, 71–78 (1993)CrossRefGoogle Scholar
  2. 2.
    Ballerini, J.: Genetic Snakes for Medical Image Segmentation. In: Poli, R., Voigt, H.-M., Cagnoni, S., Corne, D.W., Smith, G.D., Fogarty, T.C. (eds.) EvoIASP 1999 and EuroEcTel 1999. LNCS, vol. 1596, pp. 59–73. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  3. 3.
    Chen, E.L., Chung, P.C., Chen, C.L., Tsai, H.M., Chang, C.I.: An automatic diagnostic system for CT liver image classification. IEEE Transactions on Biomedical Engineering 45(6), 783–794 (1998)CrossRefGoogle Scholar
  4. 4.
    Ciecholewski, M., Dȩbski, K.: Automatic detection of liver contour in CT images. Automatics, semi-annual journal of the AGH University of Science and Technology 10(2) (2006)Google Scholar
  5. 5.
    Husain, S.A., Shigeru, E.: Use of neural networks for feature based recognition of liver region on CT images. Neural Networks for Sig. Proc. Proceedings of the IEEE Work 2, 831–840 (2000)Google Scholar
  6. 6.
    Kass, M., Witkin, A., Terauzopoulos, D.: Snakes, Active Contour Models. Int. J. Computer Vision 1(4), 259–263 (1987)Google Scholar
  7. 7.
    Meyer- Bäse, A.: Pattern Recognition for medical imaging. Elsevier Academic Press (2004)Google Scholar
  8. 8.
    Ritter, G.X., Wilson, J.N.: Computer Vision Algorithms in Image Algebra. CRC Press, Boca Raton (2000)CrossRefGoogle Scholar
  9. 9.
    Seo, K., Ludeman, L.C., Park, S., Park, J.: Efficient liver segmentation based on the spine. In: Yakhno, T. (ed.) ADVIS 2004. LNCS, vol. 3261, pp. 400–409. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Schilling, R.J., Harris, S.L.: Applied numerical methods for engineers. Brooks/Cole Publishing Com., Pacific Grove CA (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marcin Ciecholewski
    • 1
  • Krzysztof Dȩbski
    • 2
  1. 1.Institue of AutomaticsAGH University of Science and TechnologyKrakówPoland
  2. 2.Institue of Radiology and Nuclear MedicineMedical UniversityGdańskPoland

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