Automatic Segmentation of Single and Multiple Neoplastic Hepatic Lesions in CT Images

  • Marcin Ciecholewski
  • Marek R. Ogiela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)


This paper describes an automatic method for segmenting single and multiple neoplastic hepatic lesions in computed-tomography (CT) images. The structure of the liver is first segmented using the approximate contour model. Then, the appropriate histogram transformations are performed to enhance neoplastic focal lesions in CT images. To segment neoplastic lesions, images are processed using binary morphological filtration operators with the application of a parameterized mean defining the distribution of gray-levels of pixels in the image. Then, the edges of neoplastic lesions situated inside the liver contour are localized. To assess the suitability of the suggested method, experiments have been carried out for two types of tumors: hemangiomas and hepatomas. The experiments were conducted on 60 cases of various patients. Thirty CT images showed single and multiple focal hepatic neoplastic lesions, and the remaining 30 images contained no disease symptoms. Experimental results confirmed that the method is a useful tool supporting image diagnosis of the normal and abnormal liver. The proposed algorithm is 78.3% accurate.


Compute Tomography Image Neoplastic Lesion Automatic Segmentation Gaussian Smoothing 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.
    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
  2. 2.
    Ciecholewski, M., Dębski, K.: Automatic Segmentation of the Liver in CT Images Using a Model of Approximate Contour. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.) ISCIS 2006. LNCS, vol. 4263, pp. 75–84. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Danaei, G., Vander Hoorn, S., Lopez, A.D., Murray, C.J., Ezzati, M.: Causes of cancer in the world: comparative risk assessment of nine behavioural and environmental risk factors. Lancet 366, 1774–1793 (2005)CrossRefGoogle Scholar
  5. 5.
    Meyer- Bäse, A.: Pattern Recognition for medical imaging. Elsevier, Amsterdam (2004)Google Scholar
  6. 6.
    Park, S., Seo, K., Park, J.: Automatic Hepatic Tumor Segmentation Using Statistical Optimal Threshold. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3514, pp. 934–940. Springer, Heidelberg (2005)Google Scholar
  7. 7.
    Rangayan, R.M.: Biomedical signal analysis. Wiley Computer Publishing, New York (1997)Google Scholar
  8. 8.
    Ritter, G.X., Wilson, J.N.: Computer Vision Algorithms in Image Algebra. CRC Press, Boca Raton (2000)Google Scholar
  9. 9.
    Seo, K.: Automatic Hepatic Tumor Segmentation Using Composite Hypotheses. In: Kamel, M., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 922–929. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Seo, K., Chung, T.: Automatic Boundary Tumor Segmentation of a Liver. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3483, pp. 836–842. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Sonka, M., Fitzpatrick, J.M.: Handbook of Medical Imaging volume 2. Medical Image Processing and Analysis. SPIE Press, Bellingham (2000)Google Scholar
  12. 12.
    Tadeusiewicz, R., Ogiela, M.: Medical Image Understanding Technology. Springer, Heidelberg (2004)zbMATHGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Marcin Ciecholewski
    • 1
  • Marek R. Ogiela
    • 1
  1. 1.Institute of Automatics, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 KrakówPoland

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