Automatic Liver Segmentation in CT Images Using Improvised Techniques

  • Prerna KakkarEmail author
  • Sushama Nagpal
  • Nalin Nanda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10983)


Computer aided automatic segmentation of liver can serve as an elementary step for radiologists to trace anomalies in the liver. In this paper, we have explored two techniques for liver segmentation - Region growing technique of Morphological Snake and a graph-based technique called Felzenszwalb. The aforementioned techniques have been modified by incorporating Artificial Neural Network (ANN) for automatic seed generation eliminating any user intervention. It has been tested on an open-source dataset of Liver CT Scans. Compared to the algorithms that have been used in past, the algorithms discussed in this paper are computationally much efficient in terms of time. Both algorithms were able to segment liver with high accuracy but Morphological Snake outperformed Felzenszwalb in terms of segmentation by achieving a dice index of 0.88 and a very high accuracy of 98.11%. However, Felzenszwalb computed results at a faster rate.


Liver Segmentation Region-growing Graph-based Morphological Snake Neural network 


  1. 1.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  2. 2.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRefGoogle Scholar
  3. 3.
    Abd-Elaziz, O.F., Sayed, M.S., Abdullah, M.: Liver tumors segmentation from abdominal CT images using region growing and morphological processing. In: 2014 International Conference on Engineering and Technology (ICET) (2014)Google Scholar
  4. 4.
    Jayanthi, M., Kanmani, B.: Extracting the liver and tumor from abdominal CT images. In: 2014 Fifth International Conference on Signal and Image Processing (2014)Google Scholar
  5. 5.
    Patanavijit, V.: The bilateral denoising performance influence of window, spatial and radiometric variance. In: 2015 2nd International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA) (2015)Google Scholar
  6. 6.
    Point Operations - Contrast Stretching.
  7. 7.
    Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer 29(3), 31–44 (1996)CrossRefGoogle Scholar
  8. 8.
    Márquez-Neila, P., Baumela, L., Alvarez, L.: A morphological approach to curvature-based evolution of curves and surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 2–17 (2014)CrossRefGoogle Scholar
  9. 9.
    Álvarez, L., Baumela, L., Henríquez, P., Márquez-Neila, P.: Morphological snakes. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, pp. 2197–2202 (2010)Google Scholar
  10. 10.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)CrossRefGoogle Scholar
  11. 11.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)CrossRefGoogle Scholar
  12. 12.
    Oliva, M.R., Saini, S.: Liver cancer imaging: role of CT, MRI, US and PET. Cancer Imaging 4(Spec No A), S42–S46 (2004). PMC. Web. 4 Apr. (2018)CrossRefGoogle Scholar
  13. 13.
    Dheeba, J., Singh, N.A., Selvi, S.T.: Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J. Biomed. Inform. 49, 45–52 (2014)CrossRefGoogle Scholar
  14. 14.
    Kong, J., Wang, J., Lu, Y., Zhang, J., Li, Y., Zhang, B.: A novel approach for segmentation of MRI brain images. In: 2006 IEEE Mediterranean Electrotechnical Conference MELECON 2006, Malaga, pp. 525–528 (2006)Google Scholar
  15. 15.
    Tong, J., Da-Zhe, Z., Ying, W., Xin-Hua, Z., Xu, W.: Computer-aided lung nodule detection based on CT images. In: 2007 IEEE/ICME International Conference on Complex Medical Engineering, Beijing, pp. 816–819 (2007)Google Scholar
  16. 16.
    Amutha, A., Wahidabanu, R.S.D.: Lung tumor detection and diagnosis in CT scan images. In: 2013 International Conference on Communication and Signal Processing, pp. 1108–1112 (2013)Google Scholar
  17. 17.
    Selvathi, D., Malini, C., Shanmugavalli, P.: Automatic segmentation and classification of liver tumor in CT images using adaptive hybrid technique and contourlet based ELM classifier. In: 2013 International Conference on Recent Trends in Information Technology (ICRTIT), pp. 250–256 (2013)Google Scholar
  18. 18.
    Gambino, O., et al.: Automatic volumetric liver segmentation using texture based region growing. In: 2010 International Conference on Complex, Intelligent and Software Intensive Systems, Krakow, pp. 146–152 (2010)Google Scholar
  19. 19.
    Moghbel, M., Mashohor, S., Mahmud, R., Saripan, I.M.B.: Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring. EXCLI J. 15, 406–423 (2006)Google Scholar
  20. 20.
    Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., Greenspan, H.: Fully convolutional network for liver segmentation and lesions detection. In: Carneiro, G., et al. (eds.) LABELS/DLMIA-2016. LNCS, vol. 10008, pp. 77–85. Springer, Cham (2016). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Electronics and Communication DepartmentNSITDwarkaIndia
  2. 2.Computer Science DepartmentNSITDwarkaIndia

Personalised recommendations