This paper deals with Markov random field model-based image segmentation. This involves parameter estimation in hidden Markov models for which one of the most widely used procedures is the EM algorithm. In practice, difficult- ies arise due to the dependence structure in the models and approximations are required to make the algorithm tractable. We propose a class of algorithms in which the idea is to deal with systems of independent variables. This corresponds to approximations of the pixels' interactions similar to the mean field approximation. It follows algorithms that have the advantage of taking the Markovian structure into account while preserving the good features of EM. In addition, this class, that includes new and already known procedures, is presented in a unified framework, showing that apparently distant algorithms come from similar approximation principles. We illustrate the algorithms performance on synthetic and real images. These experiments point out the ability of our procedures to take the spatial information into account. Our algorithms often show significant improvement when comparing with the EM algorithm applied with no account of the spatial structure and with the ICM algorithm, based on maximization of the pseudo-likelihood and commonly used in image segmentation.