Wafa Moualhi, Ezzeddine Zagrouba



Fuzzy-c-means (FCM) algorithm is widely used for magnetic resonance (MR) image segmentation. However, conventional FCM is sensitive to noise because it does not consider the spatial information in the image. To overcome the above problem, an FCM algorithm with spatial information is presented in this paper. The algorithm is realized by integrating spatial contextual information into the membership function to make the method less sensitive to noise. The new spatial information term is defined as the summation of the membership function in the neighborhood of pixel under consideration weighted by a parameter  to control the neighborhood effect. This new method is applied to both synthetic images and MR data. Experimental results show that the presented method is more robust to noise than the conventional FCM and yields homogenous labeling.


  1. Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A. and Moriarty, T., 2002. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging, v21, 193-199.
  2. Bezdek(a), J., Hall, L., Clarke, L., 1993. Review of MR image segmentation using pattern recognition. Med Phys, v20, 1033-1048.
  3. Bezdek(b), JC., 1974. Cluster validity with fuzzy sets. J Cybern, v3, 58-72.
  4. Bezdek(c), JC., 1975. Mathematical models for systematic and taxonomy. In: proceedings of eigth international conference on numerical taxonomy, San Francisco, pp 143-166.
  5. Chen(a), W.J., Giger, M.L., Bick, U., 2006. A fuzzy cmeans (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast enhanced MRI images, Acad. Radiol, Vol. 13, No. 1, pp. 63-72.
  6. Chen(b), S.C., Zhang, D.Q., 2004. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure, IEEE Trans. Syst. Man Cybern. B, vol.34, no.4, pp.1907- 1916.
  7. Goldszal, A. F., Davatzikos, C., Pham, D. L., Yan, M. X., Bryan, H. R. N. and Resnick, S. M, 1998. “An image processing system for qualitative and quantitative volumetric analysis of brain images,” J. Comput. Assist. Tomog, 22(5):827-37.
  8. Leemput, K. V., Maes, F., Vandermeulen, D. and In Suetens, P., 1999. Automated model-based tissue classification of MR images of the brain, IEEE Trans. Med. Imag, vol. 18, no 10, pp. 897-908.
  9. Lyer, NS., Kandel, A., Schneider, M., 2002. Featurebased fuzzy classification for interpretation of mammograms. Fuzzy Sets Syst, 114:, pp. 271-80.
  10. Pham, D.L., Prince, J.L., 1999. An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities, Pattern Recognition Letters, v.20 n.1, pp. 57-68.
  11. Shen, S., Sandham, W., Grant, M., and Ster, A., 2005. MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction with Neural-Network Optimization, IEEE Trans. Inform. Technol. Biomed. v9 i3. 459-467.
  12. Yang, MS, Hu, YJ, Lin, KCR, 2002. (FCM)- based Segmentation techniques for tissue differentiation in MRI of Ophthalmology using fuzzy clustering algorithms. Magn Reson Imaging, 20: pp.173-179.

Paper Citation

in Harvard Style

Moualhi W. and Zagrouba E. (2009). ROBUST FUZZY-C-MEANS FOR IMAGE SEGMENTATION . In Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009) ISBN 978-989-8111-68-5, pages 87-91. DOI: 10.5220/0001787000870091

in Bibtex Style

author={Wafa Moualhi and Ezzeddine Zagrouba},
booktitle={Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)},

in EndNote Style

JO - Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)
SN - 978-989-8111-68-5
AU - Moualhi W.
AU - Zagrouba E.
PY - 2009
SP - 87
EP - 91
DO - 10.5220/0001787000870091