Image Segmentation by Gaussian Mixture Models and Modified FCM Algorithm

Karim Kalti and Mohamed Ali Mahjoub
 Research Unit SAGE, Team Signals, Image and Document, National Engineering School of Sousse
University of Sousse-Tunisia

Abstract: The Expectation Maximization (EM) algorithm and the clustering method “Fuzzy-C-Means” (FCM) are widely used in image segmentation. However, the major drawback of these methods is their sensitivity to the noise. In this paper, we propose a variant of these methods which aim at resolving this problem. Our approaches proceed by the characterization of pixels by two features: the first one describes the intrinsic properties of the pixel and the second characterizes the neighborhood of pixel.  Then the classification is made on the base on adaptive distance which privileges the one or the other features according to the spatial position of the pixel in the image. The obtained results have shown a significant improvement of our approaches performance compared to the standard version of the EM and FCM respectively, especially regarding about the robustness face to noise and the accuracy of the edges between regions.

Keywords: EM algorithm, FCM algorithm, image segmentation, adaptive distance.

 

Received May 4, 2011; accepted December 30, 2012

  

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