This work investigates a new partial volume (PV) image segmentation framework with comparison to a previous PV approach. The new framework utilizes an expectation-maximization (EM) algorithm to estimate simultaneously (1) tissue fractions in each image voxel and (2) statistical model parameters of the image data under the principle of maximum a posteriori probability (MAP). The previous EM approach models the PV effect by down-sampling a voxel and then labels each sub-voxel as a pure tissue type, where the number of sub-voxels labeled by a given tissue type over the total number of sub-voxels reflects the fraction of that tissue type inside the original voxel. The tissue fractions in each voxel in this discrete PV model are represented by a limited number of percentage values. In the new MAP-EM approach, the PV effect is modeled in a continuous space and estimated directly as the fraction of each tissue type in the original voxel. The previous discrete PV model would converge to our continuous PV tissue-mixture model if there is an infinite number of sub-voxels within a voxel. However, in practice a voxel is usually down-sampled once or twice for computational reasons. A comparison study between this limited down-sampling approach and our continuous PV model reveals, by computer simulations, that our continuous PV model is computationally more effective and thus improves the PV segmentation over the discrete PV model.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.