An EM approach to MAP solution of segmenting tissue mixture percentages with application to CT-based virtual colonoscopy.

Electronic colon cleansing (ECC) is an emerging technique developed to segment the colon lumen from a patient's abdominal computed tomography colonography (CTC) images. However, the residue stool and fluid tagged by contrast materials as well as mixed tissue distribution with partial volume (PV) effect impose several challenges for ECC, resulting in incomplete and overcomplete cleansings. To address the PV effect, this work investigated an improved maximum a posteriori expectation-maximization (MAP-EM) image segmentation algorithm which simultaneously estimates tissue mixture percentages within each image voxel and statistical model parameters for the tissue distribution. Given the segmented tissue mixture information beyond the image voxel level, not only the PV effect has been satisfactorily addressed as a particular case of tissue mixture problem, but incomplete and overcomplete ECC causes could also be maximally avoided. For clinical application to CTC that involves several issues transferring from theoretical analysis to practical validation, an innovative initialization procedure and refined estimation strategy were proposed to build an ECC pipeline based on the MAP-EM segmentation. The pipeline was evaluated based on 52 patient CTC studies, downloaded from the website of the Virtual Colonoscopy Screening Resource Center, by two radiologists. A noticeable improvement over the authors' previous ECC pipeline was documented. Several typical cases were also presented to show visually the improved performance of the presented ECC pipeline.