Segmentation of noisy images using the rank M-type L-filter and the fuzzy C-means clustering algorithm

Authors:
Dante Mújica-Vargas;Francisco J. Gallegos-Funes;Rene Cruz-Santiago
Affiliations:
Mechanical and Electrical Engineering Higher School, National Polytechnic Institute of Mexico;Mechanical and Electrical Engineering Higher School, National Polytechnic Institute of Mexico;Mechanical and Electrical Engineering Higher School, National Polytechnic Institute of Mexico
Venue:
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Year:
2011

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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.