title: Automatic classification of images with appendiceal orifice in colonoscopy videos.
abstract: Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. In current practice, videos captured from colonoscopic procedures are not routinely stored for either manual or automated post-procedure analysis. In this paper, we introduce new algorithms for automated detection of the presence of the shape of the opening of the appendix in a colonoscopy video frame. The appearance of the appendix in colonoscopy videos indicates traversal of the colon, which is an important measurement for evaluating the quality of colonoscopic procedures. The proposed techniques are valuable for (1) establishment of an effective content-based retrieval system to facilitate endoscopic research and education; and (2) assessment and improvement of the procedural skills of endoscopists, both in training and practice.
authors.affiliation: Department of Computer Science, Iowa State University, Ames, IA 50011, USA. impact.isu@cs.iastate.edu
authors.forename: Yu
authors.initials: Y
authors.lastname: Cao
authors.forename: Danyu
authors.initials: D
authors.lastname: Liu
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; D053704:Capsule Endoscopy / Q000379:methods*; D003106:Colon / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity