Computed tomography-based virtual colonoscopy or CT colonography (CTC) currently utilizes oral contrast solution to differentiate the colonic fluid and possibly residual stool from the colon wall. The enhanced image density of the tagged colonic materials causes a significant partial volume (PV) effect into the colon wall as well as the lumen space (air or CO2). The PV effect into the colon wall can "bury" polyps of small size by increasing their image densities to a noticeable level, resulting in false negatives. It can also create false positives when PV effect goes into the lumen space. Modeling the PV effect for mixture-based image segmentation has been a research topic for many years. This paper presents the practical implementation of our newly developed statistical image segmentation framework, which utilizes the EM (expectation-maximization) algorithm to estimate (1) tissue fractions in each image voxel and (2) statistical model parameters of the image under the principle of maximum a posteriori probability (MAP). This partial-volume expectation-maximization (PV-EM) mixture-based MAP image segmentation pipeline was tested on 52 CTC datasets downloaded from the website of the VC Screening Resource Center, with each dataset consisting of two scans of supine and prone positions, resulting in 104 CT volume images. The cleansed lumens by the automated PV-EM image segmentation algorithm were visualized with comparison to our previous work, with the gain achieved mainly in the following three aspects: (1) the tissue fraction information of those voxels with PV effect have been well preserved, (2) the problem of incomplete cleansing of tagged materials in our previous work has been mitigated, and (3) the interference caused by small bowel was significantly released.