2 May 2003 Automatic segmentation of colonic polyps in CT colonography based on knowledge-guided deformable models
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An automatic method to segment colonic polyps from CT colonography is presented. The method is based on a combination of knowledge-guided intensity adjustment, fuzzy c-mean clustering, and deformable models. The input is a set of polyp seed points generated by filters on geometric properties of the colon surface. First, the potential polyp region is enhanced by a knowledge-guided adjustment. Then, a fuzzy c-mean clustering is applied on a 64*64 pixel sub-image around the seed. Fuzzy membership functions for lumen air, polyp tissues and other tissues are computed for each pixel. Finally, the gradient of the fuzzy membership function is used as the image force to drive a deformable model to the polyp boundary. The segmentation process is first executed on the 2D transverse slice where the polyp seed is located, and then is propagated to neighboring slices to construct a 3D representation of the polyp. Manual segmentation is performed on the same polyps and treated as the ground truth. The automatically generated segmentation is compared with the ground truth segmentation to validate the accuracy of the method. Experimental results showed that the average overlap between the automatic segmentation and manual segmentation is 76.3%. Given the complex polyp boundaries and the small size of the polyp, this is a good result both visually and quantitatively.
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Jianhua Yao, Jianhua Yao, Meghan Miller, Meghan Miller, Marek Franaszek, Marek Franaszek, Ronald M. Summers, Ronald M. Summers, } "Automatic segmentation of colonic polyps in CT colonography based on knowledge-guided deformable models", Proc. SPIE 5031, Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications, (2 May 2003); doi: 10.1117/12.480415; https://doi.org/10.1117/12.480415

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