An EM framework for segmentation of tissue mixtures from medical images

Image segmentation plays a major role in quantitative image analysis and computer aided detection (CAD) and diagnosis (CADx) for clinical applications. Conventional segmentation assigns a single label to each voxel, neglecting the partial volume (PV) effect. This work presents an EM (expectation maximization) framework for segmentation of tissue mixture in each voxel. Image data and tissue mixture models, EM algorithm for mixture quantification, prior model for regularization on the mixtures, and multi-spectral MR (magnetic resonance) data characterization are described in details. Preliminary results from CT (computed tomography) and MR images are reported to demonstrate its potential for clinical use.