title: Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models.
abstract: An automatic method to segment colonic polyps in computed tomography (CT) colonography is presented in this paper. The method is based on a combination of knowledge-guided intensity adjustment, fuzzy c-mean clustering, and deformable models. The computer segmentations were compared with manual segmentations to validate the accuracy of our method. An average 76.3% volume overlap percentage among 105 polyp detections was reported in the validation, which was very good considering the small polyp size. Several experiments were performed to investigate the intraoperator and interoperator repeatability of manual colonic polyp segmentation. The investigation demonstrated that the computer-human repeatability was as good as the interoperator repeatability. The polyp segmentation was also applied in computer-aided detection (CAD) to reduce the number of false positive (FP) detections and provide volumetric features for polyp classification. Our segmentation method was able to eliminate 30% of FP detections. The volumetric features computed from the segmentation can further reduce FP detections by 50% at 80% sensitivity.
authors.affiliation: Diagnostic Radiology Department, Clinical Center, the National Institutes of Health, 10 Center Drive, MSC 1182, Bethesda, MD 20892-1182 USA.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Yao
authors.forename: Meghan
authors.initials: M
authors.lastname: Miller
authors.forename: Marek
authors.initials: M
authors.lastname: Franaszek
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Summers
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003111:Colonic Polyps / Q000145:classification / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D004548:Elasticity; D017143:Fuzzy Logic; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted