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)

Abstract

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.

Keywords

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

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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

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