Investigation on an EM framework for partial volume image segmentation

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 subvoxel as a pure tissue type, where the number of subvoxels labeled by a given tissue type over the total number of subvoxels 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 the proposed continuous PV tissue-mixture model if there is an infinite number of subvoxels within a voxel. However, in practice a voxel is usually down-sampled once or twice for computational reasons. A simulation study reveals that the continuous PV model is not only more realistic but also more accurate than the discrete PV model.