title: Edge cross-section features for detection of appendiceal orifice appearance in colonoscopy videos.
abstract: Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. The appearance of the appendiceal orifice during colonoscopy indicates a complete traversal of the colon, which is one of important quality indicators of examination of the colon. In this paper, we propose a new algorithm that detects appendix images-images showing the appendiceal orifice. We introduce new features based on geometric shape, saturation and intensity changes along the norm direction (cross-section) of an edge to discriminate appendix images. Our experimental results on real colonoscopic images show the average sensitivity and specificity of 88.12% and 94.25%, respectively.
authors.affiliation: Department of Computer Science, Iowa State University, Ames, IA 50011, USA.
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
authors.forename: Wallapak
authors.initials: W
authors.lastname: Tavanapong
authors.forename: Johnny
authors.initials: J
authors.lastname: Wong
authors.forename: JungHwan
authors.initials: J
authors.lastname: Oh
authors.forename: Piet C
authors.initials: PC
authors.lastname: de Groen
mesh_terms: D000465:Algorithms; D001065:Appendix / Q000033:anatomy & histology*; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003106:Colon / Q000033:anatomy & histology*; D003113:Colonoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D025321:Surgery, Computer-Assisted / Q000379:methods*; D014741:Video Recording / Q000379:methods