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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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. © 2011 Springer-Verlag Berlin Heidelberg.
Original languageAmerican English
Title of host publicationSegmentation of noisy images using the rank m-type l-filter and the fuzzy c-means clustering algorithm
Pages184-193
Number of pages164
ISBN (Electronic)9783642215865
DOIs
StatePublished - 14 Jul 2011
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2014 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6718 LNCS
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/14 → …

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Mújica-Vargas, D., Gallegos-Funes, F. J., & Cruz-Santiago, R. (2011). Segmentation of noisy images using the rank m-type l-filter and the fuzzy c-means clustering algorithm. In Segmentation of noisy images using the rank m-type l-filter and the fuzzy c-means clustering algorithm (pp. 184-193). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6718 LNCS). https://doi.org/10.1007/978-3-642-21587-2_20