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17 March 2008 Mosaic decomposition method for detection and removal of inhomogeneously tagged regions in electronic cleansing for CT colonography
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Electronic cleansing (EC) is a method that segments fecal material tagged by an X-ray-opaque oral contrast agent in CT colonography (CTC) images, and effectively removes the material for digitally cleansing the colon. In this study, we developed a novel EC method, called mosaic decomposition, for reduction of the artifacts due to incomplete cleansing of heterogeneously tagged fecal material in reduced- or non-cathartic fecal-tagging CTC examinations. In our approach, a segmented colonic lumen, including the tagged regions, was first partitioned into a set of local homogeneous regions by application of a watershed transform to the gradient of the CTC images. Then, each of the local homogeneous regions was classified into five different material classes, including air, soft tissue, tagged feces, air bubbles, and foodstuff, based on texture features of the tile. A single index, called a soft-tissue index, is formulated for differentiation of these materials from the submerged solid soft-tissue structures such as polyps and folds. Here, a larger value of the index indicates a higher likelihood of soft tissue. Then, EC is performed by first initializing the level-set front with the classified tagged regions, and the front is evolved by use of a speed function that was designed, based on the soft-tissue index, to reserve the submerged soft-tissue structures while suppressing the air bubbles and foodstuff. Visual assessment and application of our computer-aided detection (CAD) of polyps showed that the use of our new EC method substantially improved the detection performance of CAD, indicating the effectiveness of our EC method in reducing incomplete cleansing artifacts.
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Wenli Cai, Micheal Zalis M.D., and Hiroyuki Yoshida "Mosaic decomposition method for detection and removal of inhomogeneously tagged regions in electronic cleansing for CT colonography", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69150D (17 March 2008);

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