A Markov random field (MRF) approach to image segmentation is described. Unlike most previous MRF techniques, which are based on pixel classification, this approach groups pixels that are similar. This removes the need to know the number of image classes. Mean field theory and multigrid processing are used in the subsequent optimization to find a good segmentation and to alleviate local minimum problems. Variations of the MRF approach are investigated by incorporating features/schemes motivated by characteristics of the human vision system (HVS). Experimental results are promising and indicate that multigrid and HVS-based features/schemes can improve segmentation results.