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Variational Approach Based Image Pre- Processing Techniques for Virtual Colonoscopy
Abstract
Colorectal cancer includes cancer of the colon, rectum, anus and appendix. Since it is largely preventable, it is extremely important to detect and treat the colorectal cancer in the earliest stage. Virtual colonoscopy is an emerging screening technique for colon cancer. One component of virtual colonoscopy, image preprocessing, is important for colonic polyp detection/diagnosis, feature extraction and classification. This chapter aims at an accurate and fast colon segmentation algorithm and a general variational-approach based framework for image pre-processing techniques, which include 3D colon isosurface generation and 3D centerline extraction for navigation. The proposed framework has been validated on 20 real CT Colonography (CTC) datasets. The average segmentation accuracy has achieved 96.06%, and it just takes about 5 minutes for a single CT scan of 512*512*440. All the 12 colonic polyps with sizes of 6 mm and above in the 20 clinical CTC datasets are found by this work.
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