  This paper introduces a Bayesian image segmentation algorithm based on finite
mixtures. An EM algorithm is developed to estimate parameters of the Gaussian
mixtures. The finite mixture is a flexible and powerful probabilistic modeling
tool. It can be used to provide a model-based clustering in the field of
pattern recognition. However, the application of finite mixtures to image
segmentation presents some difficulties; especially it's sensible to noise. In
this paper we propose a variant of this method which aims to resolve this
problem. Our approach proceeds 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 approach compared to the
standard version of EM algorithm.
