Virtual colonoscopy provides a safe, minimal-invasive approach to detect colonic polyps using medical imaging and computer graphics technologies. Residual stool and fluid are problematic for optimal viewing of the colonic mucosa. Electronic cleansing techniques combining bowel preparation, oral contrast agents, and image segmentation were developed to extract the colon lumen from computed tomography (CT) images of the colon. In this paper, we present a new electronic colon cleansing technology, which employs a hidden Markov random filed (MRF) model to integrate the neighborhood information for overcoming the non-uniformity problems within the tagged stool/fluid region. Prior to obtaining CT images, the patient undergoes a bowel preparation. A statistical method for maximum a posterior probability (MAP) was developed to identify the enhanced regions of residual stool/fluid. The method utilizes a hidden MRF Gibbs model to integrate the spatial information into the Expectation Maximization (EM) model-fitting MAP algorithm. The algorithm estimates the model parameters and segments the voxels iteratively in an interleaved manner, converging to a solution where the model parameters and voxel labels are stabilized within a specified criterion. Experimental results are promising.

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A computer-graphics-based alternative to conventional optical colonoscopy, known as virtual colonoscopy (VC) or computed tomography colonography (CTC), is rapidly gaining popularity. During this procedure, which was concurrently developed by our group at Stony Brook University [5] and by other researchers [10], the distended colon is imaged by a helical or multislice CT scanner. The acquired abdominal CT scan commonly consists of 350-750 axial images of 512x512 submillimeter resolution, providing excellent contrast between the colon wall and the lumen. A 3D model of the colon is then reconstructed from the CT scan by automatically segmenting the colon out of the rest of the abdomen and employing an electronic cleansing algorithm for computer-based removal of the residual

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