Efficient Feature Extraction for Fast Segmentation of MR Brain Images

  • László Szilágyi
  • Sándor M. Szilágyi
  • Zoltán Benyó
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


Automated brain MR image segmentation is a challenging problem and received significant attention lately. Various techniques have been proposed, several improvements have been made to the standard fuzzy c-means (FCM) algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. In this paper we present a modified FCM algorithm, which aims at accurate segmentation in case of mixed noises, and performs at a high processing speed. As a first step, a scalar feature value is extracted from the neighborhood of each pixel, using a filtering technique that deals with both spatial and gray level distances. These features are clustered afterwards using the histogram-based approach of the enhanced FCM algorithm. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides better results compared to other reported FCM-based techniques. The produced segmentation and fuzzy membership values can serve as excellent support for level set based cortical surface reconstruction techniques.


image segmentation fuzzy c-means algorithm feature extraction noise elimination magnetic resonance imaging 


  1. 1.
    Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imag. 21, 193–199 (2002)CrossRefGoogle Scholar
  2. 2.
    Bezdek, J.C., Pal, S.K.: Fuzzy models for pattern recognition. IEEE Press, Piscataway (1991)Google Scholar
  3. 3.
    Cai, W., Chen, S., Zhang, D.Q.: Fast and robust fuzzy c-means algorithms incorporating local information for image segmentation. Patt. Recogn. 40, 825–838 (2007)CrossRefzbMATHGoogle Scholar
  4. 4.
    Chen, S., Zhang, D.Q.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Syst. Man. Cybern. Part. B 34, 1907–1916 (2004)CrossRefGoogle Scholar
  5. 5.
    Chuang, K.S., Tzeng, H.L., Chen, S., Wu, J., Chen, T.J.: Fuzzy c-means clustering with spatial information for image segmentation. Comp. Med. Imag. Graph. 30, 9–15 (2006)CrossRefGoogle Scholar
  6. 6.
    Hathaway, R.J., Bezdek, J.C., Hu, Y.: Generalized fuzzy c-means clustering strategies using L p norm distances. IEEE Trans. Fuzzy Syst. 8, 576–582 (2000)CrossRefGoogle Scholar
  7. 7.
    Internet Brain Segmentation Repository, at
  8. 8.
    Pham, D.L., Prince, J.L.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imag. 18, 737–752 (1999)CrossRefGoogle Scholar
  9. 9.
    Pham, D.L.: Unsupervised tissue classification in medical images using edge-adaptive clustering. In: Proc. Ann. Int. Conf. IEEE EMBS, vol. 25, pp. 634–637 (2003)Google Scholar
  10. 10.
    Siyal, M.Y., Yu, L.: An intelligent modified fuzzy c-means based algorithm for bias field estimation and segmentation of brain MRI. Patt. Recogn. Lett. 26, 2052–2062 (2005)CrossRefGoogle Scholar
  11. 11.
    Szilágyi, L., Benyó, Z., Szilágyi, S.M., Adam, H.S.: MR brain image segmentation using an enhanced fuzzy C-means algorithm. In: Proc. Ann. Int. Conf. IEEE EMBS, vol. 25, pp. 724–726 (2003)Google Scholar
  12. 12.
    Szilágyi, L.: Medical image processing methods for the development of a virtual endoscope. Period. Polytech. Ser. Electr. Eng. 50(1-2), 69–78 (2006)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • László Szilágyi
    • 1
    • 2
  • Sándor M. Szilágyi
    • 2
  • Zoltán Benyó
    • 1
  1. 1.Budapest University of Technology and Economics, Dept. of Control Engineering and Information Technology, BudapestHungary
  2. 2.Sapientia - Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Târgu-MureşRomania

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