Fast Uncertainty-Guided Fuzzy C-Means Segmentation of Medical Images

  • Ahmed Al-Taie
  • Horst K. Hahn
  • Lars Linsen
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


Image segmentation is a crucial step of the medical visualization pipeline. In this paper, we present a novel fast algorithm for modified fuzzy c-means segmentation of MRI data. The algorithm consists of two steps, which are executed as two iterations of a fuzzy c-means approach: the first iteration is a standard fuzzy c-means (FCM) iteration, while the second iteration is our modified FCM iteration with misclassification correction. In the second iteration, we use the classification probability vectors (uncertainties) of the neighbor pixels found by the first iteration to confirm or correct the classification decision of the current pixel. The application of the proposed algorithm on synthetic data, simulated MRI data, and real MRI data show that our algorithm is insensitive to different types of noise and outperforms the standard FCM and several versions of modified FCM algorithms in terms of accuracy and speed. In fact, our algorithm can easily be combined with many modified FCM algorithms to improve their segmentation result while reducing the computation costs (using two FCM iterations only). An optional simple post-processing step can further improve the segmentation result by correcting isolated misclassified pixels. We also show that our algorithm reduces the uncertainty in the segmentation result, by using recently proposed uncertainty estimation and visualization tools.


Synthetic Image Segmentation Accuracy Noisy Pixel Fuzzy Partition Matrix Neighborhood Majority 
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.


  1. 1.
    Guttmann, C., Jolesz, F.A., Kikinis, R., Killiany, R., Moss, M., Sandor, T., Albert, M.: White matter changes with normal aging. Neurology 50, 972–978 (1998)CrossRefGoogle Scholar
  2. 2.
    Heindel, W.C., Jernigan, T.L., Archibald, S.L., Achim, C.L., Masliah, E., Wiley, C.A.: The relationship of quantitative brain magnetic resonance imaging measures to neuropathologic indexes of human immunodeficiency virus infection. Arch. Neurol. 51, 1129–35 (1994)CrossRefGoogle Scholar
  3. 3.
    Mohamed, N.A., Ahmed, M.N., Farag, A.: Modified fuzzy c-mean in medical image segmentation. In: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, Piscataway, NI, USA, vol. 6, pp. 3429–3432 (1999)Google Scholar
  4. 4.
    Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., and Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging 21(3), 193–199Google Scholar
  5. 5.
    Pham, D.L.: Fuzzy clustering with spatial constraints. In: 2002 Proceedings International Conference on Image Processing, vol. 2, pp. II–65–II–68 (2002)Google Scholar
  6. 6.
    Chen S., Zhang, D.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance metric. IEEE Trans. Syst. Man Cybern. B 34(4), 1907–1916 (2004)CrossRefGoogle Scholar
  7. 7.
    Zhang, D., Chen, S.: A novel kernelised fuzzy c-means algorithm with application in medical image segmentation. Artif. Intell. Med. 32(1), 37–50 (2004)CrossRefGoogle Scholar
  8. 8.
    Yuan, K., Wu, L., Cheng, Q.S., Bao, S., Chen, C., Zhang, H.,: A novel fuzzy c-means algorithm and its application. Int. J. Pattern Recognit. Artif. Intell. 19(8), 1059–1066 (2005)CrossRefGoogle Scholar
  9. 9.
    Chuang, K.-S., Tzeng, H.-L., Chen, S., Wu, J., Chen, T.-J.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 30(1), 9–15 (2006)CrossRefGoogle Scholar
  10. 10.
    Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn. 40(3), 825–838 (2007)CrossRefzbMATHGoogle Scholar
  11. 11.
    Tolias, Y.A., Panas, S.M.: On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system. IEEE Signal Process Lett. 5(10), 245–247 (1998)CrossRefGoogle Scholar
  12. 12.
    Saad, A., Möller, T. and Hamarneh, G.: Probexplorer: uncertainty-guided exploration and editing of probabilistic medical image segmentation. Comput. Graphics Forum 29(3), 1113–1122 (2010)CrossRefGoogle Scholar
  13. 13.
    Olabarriagaa S.D., Smeuldersb A.W.M.: Interaction in the segmentation of medical images: a survey. Med. Image Anal. 5, 127–142 (2001)CrossRefGoogle Scholar
  14. 14.
    Al-Taie, A., Hahn, H.K., Linsen, L.: Uncertainty estimation and visualization in probabilistic segmentation. Comput. Graph. 39(0), 48–59 (2014); Available online: 26 October 2013Google Scholar
  15. 15.
    El-Melegy, M.T., Mokhtar, H.: Incorporating prior information in the fuzzy c-mean algorithm with application to brain tissues segmentation in MRI. In: International Conference on Image Processing (ICIP), pp. 3393–3396. IEEE (2009)Google Scholar
  16. 16.
    Li, C., Xu, C., Anderson, A.W., Gore, J.C.: MRI tissue classification and bias field estimation based on coherent local intensity clustering: a unified energy minimization framework. In Prince, J.L., Pham, D.L., Myers, K.J. (eds.) Information Processing in Medical Imaging. Lecture Notes in Computer Science, vol. 5636, pp. 288–299. Springer, Berlin/Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Wang, J., Kong, J., Lu, Y., Qi, M., Zhang, B.: A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints. Comput. Med. Imaging Graph. 32(8), 685–698 (2008)CrossRefGoogle Scholar
  18. 18.
    Ji, Z.-X., Sun, Q.-S., Xia, D.-S.: A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain mr image. Comput. Med. Imaging Graph. 35(5), 383–397 (2011)CrossRefGoogle Scholar
  19. 19.
    Szilágyi, L., Benyo, Z., Szilágyi, S.M., Adam, H.S.: Mr brain image segmentation using an enhanced fuzzy c-means algorithm. In: Proceedings of the 25th Annual International Conference of the IEEE, vol. 1, pp. 724–726. Engineering in Medicine and Biology Society (2003)Google Scholar
  20. 20.
    Praßni, J.S., Ropinski, T., Hinrichs, K.: Uncertainty-aware guided volume segmentation. IEEE Trans. Vis. Comput. Graph. 16(6), 1358–1365 (2010)CrossRefGoogle Scholar
  21. 21.
    Potter, K.C., Gerber, S., Anderson, E.W.: Visualization of uncertainty without a mean. IEEE Comput. Graph. Appl. 33(1), 75–79 (2013)CrossRefGoogle Scholar
  22. 22.
    Bezdek J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)CrossRefzbMATHGoogle Scholar
  23. 23.
    MNI. Brainweb, Simulated Brain Database: Available since 1997. Available at, access time: on November 2012, 1997
  24. 24.
    IBSR. The Internet Brain Segmentation Repository (IBSR): Available since 1996. Available at, access time: on October 2012, 1996

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Jacobs UniversityBremenGermany
  2. 2.Baghdad UniversityBaghdadIraq
  3. 3.Fraunhofer MEVISBremenGermany

Personalised recommendations