Robust RML estimator - fuzzy c-means clustering algorithms for noisy image segmentation

Authors:
Dante Mújica-Vargas;Francisco Javier Gallegos-Funes;Alberto J. Rosales-Silva;Rene Cruz-Santiago
Affiliations:
Mechanical and Electrical Engineering Higher School, National Polytechnic Institute of Mexico, México, D.F., México;Mechanical and Electrical Engineering Higher School, National Polytechnic Institute of Mexico, México, D.F., México;Mechanical and Electrical Engineering Higher School, National Polytechnic Institute of Mexico, México, D.F., México;Mechanical and Electrical Engineering Higher School, National Polytechnic Institute of Mexico, México, D.F., México
Venue:
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Year:
2011

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Abstract

Image segmentation is a key step for many images analysis applications. So far, there does not exist a general method to segment suitable all images, regardless if these are corrupted or noise free. In this paper, we propose to modify the Fuzzy C-means clustering algorithm and the FCM_S1 variant by using the RML-estimator. The idea to our method is to get robust clustering algorithms able to segment images with different type and levels of noises. The performance of the proposed algorithms is tested on synthetic and real images. Experimental results show that the proposed algorithms are more robust to the noise presence and more effective than the comparative algorithms.