The present work formulates a novel segmentation algorithm which combines the use of Markov random field models for image-modeling with the use of the discrete wave-packet transform for image analysis. Image segmentations are derived and refined at a sequence of resolution levels, using as data selected wave-packet transform images or ''channels.'' The described segmentation algorithm is compared with non-multiresolution Markov random field-based image segmentation algorithms in the contest of synthetic image example problems, and found to be both significantly more efficient and effective.