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Image segmentation plays an important role in medical image processing. The aim of conventional hard segmentation methods is to assign a unique label to each voxel. However, due to the limited spatial resolution of medical imaging equipment and the complex anatomic structure of soft tissues, a single voxel in a medical image may be composed of several tissue types, which is called partial volume (PV) effect. Using the hard segmentation methods, the PV effect can substantially decrease the accuracy of quantitative measurements and the quality of visualizing different tissues. In this paper, instead of labeling each voxel with a unique label or tissue type, the percentage of different tissues within each voxel, which we call a mixture, was considered in establishing an image segmentation framework of maximum a posterior (MAP) probability. A new Markov random field (MRF) model was used to reflect the spatial information for the tissue mixture. Parameters of each tissue class were estimated through the expectation-maximization (EM) algorithm during the MAP tissue mixture segmentation. The MAP-EM mixture segmentation methodology was tested by digital phantom MR and patient CT images with PV effect evaluation. Results demonstrated that a hard segmentation method would lose a significant amount of details along the tissue boundaries, while the presented new PV segmentation method can dramatically improve the performance of preserving the details.