Segmentation of Noisy Images Using the Rank M-type L-filter and the Fuzzy C-Means Clustering Algorithm

  • Dante Mújica-Vargas
  • Francisco J. Gallegos-Funes
  • Rene Cruz-Santiago
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)


In this paper we present an image processing scheme to segment noisy images based on a robust estimator in the filtering stage and the standard Fuzzy C-Means (FCM) clustering algorithm to segment the images. The main objective of paper is to evaluate the performance of the Rank M-type L-filter with different influence functions and to establish a reference base to include the filter in the objective function of FCM algorithm in a future work. The filter uses the Rank M-type (RM) estimator in the scheme of L-filter, to get more robustness in the presence of different types of noises and a combination of them. Tests were made on synthetic and real images subjected to three types of noise and the results are compared with six reference modified Fuzzy C-Means methods to segment noisy images.


robust estimators RM-estimator L-filter Fuzzy C-Means segmentation noise 


  1. 1.
    Kim, J., Fisher, J.W., Yezzi, A., Cetin, M., Willsky, A.S.: A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Transactions on Image Processing, 1486–1502 (2005)Google Scholar
  2. 2.
    Dong, G., Xie, M.: Color clustering a nd learning for image segmentation based on neural networks. IEEE Transactions on Neural Networks 16(4), 925–936 (2005)CrossRefGoogle Scholar
  3. 3.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRefzbMATHGoogle Scholar
  4. 4.
    Egmont-Petersen, M., de Ridder, D., Handels, H.: Image processing with neural networks – a review. Institute of Information and Computing, Utrecht University, Utrecht (2002)Google Scholar
  5. 5.
    Gallegos-Funes, F.J., Linares, R., Ponomaryov, V., Cruz-Santiago, R.: Real-Time image processing using the Rank M-type L-filter. Cientific 11, 189–198 (2007)Google Scholar
  6. 6.
    Pitas, A.N.V.: Nonlinear Digital Filters. Kluwer Academic Publishers, Boston (1990)CrossRefzbMATHGoogle Scholar
  7. 7.
    Astola, J., Kousmanen, P.: Fundamentals of Nonlinear Digital FIltering. CRC Press, Boca Raton (1997)Google Scholar
  8. 8.
    Hampel, F.R., Ronchetti, E.M., Rouseew, P.J., Stahel, W.A.: Robust Statistics. In: The approach based on influence function. Wiley, NY (1986)Google Scholar
  9. 9.
    Gallegos-Funes, F.J., Ponomaryov, V.: Real-time image filtering scheme based on robust estimators in presence de noise impulsive. Real Time Imaging 8(2), 78–90 (2004)Google Scholar
  10. 10.
    Gallegos-Funes, F.J., Varela-Benitez, J.L., Ponomaryov, V.: Real-time image processing based on robust linear combinations of order statistics. In: Proc. SPIE Real-Time Image Processing, vol. 6063, pp. 177–187 (2006)Google Scholar
  11. 11.
    Varela-Benitez, J.L., Gallegos-Funes, F.J., Ponomaryov, V.: RML-filters for real rime imaging. In: Proc. IEEE 15th International Conference on Computing, CIC 2006, pp. 43–48 (2006)Google Scholar
  12. 12.
    Aizenberg, L., Astola, J., Breging, T., Butakoff, C., Egiazarian, K., Paily, D.: Detectors of the impulsive noise and new effective filters for the impulse noise reduction. In: Proc. SPIE Image Processing, Algorithms and Systems II, vol. 5014, pp. 410–428 (2003)Google Scholar
  13. 13.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Elsevier, Amsterdam (2009)zbMATHGoogle Scholar
  14. 14.
    Cai, W.L., Chen, S.C., Zhang, D.Q.: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition 40, 383–825 (2007)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dante Mújica-Vargas
    • 1
  • Francisco J. Gallegos-Funes
    • 1
  • Rene Cruz-Santiago
    • 1
  1. 1.Mechanical and Electrical Engineering Higher SchoolNational Polytechnic Institute of MexicoMéxico D. F.México

Personalised recommendations