One method for image segmentation involves fitting a mixture model to features extracted form an image, then using this statistical model to segment the image according to the posterior probabilities associated with each component. This procedure has the disadvantage that it can produce a noisy and disconnected segmentation. Using the posterior probabilities from the mixture, a Maximum A Posteriori (MAP) estimator can be produced which smooths the segmentation. This in turn can be used to improve the original mixture estimates via the expectation maximization (EM) algorithm for mixture models. This has the dual benefit of incorporating spatial information into the estimation of the mixture parameters, as well as producing improved segmentation. The algorithm is described, and applied to synthetic and real images. The result on the synthetic images show both improved segmentation and improved estimation of the mixture parameters.