title: Online scene association for endoscopic navigation.
abstract: Endoscopic surveillance is a widely used method for monitoring abnormal changes in the gastrointestinal tract such as Barrett's esophagus. Direct visual assessment, however, is both time consuming and error prone, as it involves manual labelling of abnormalities on a large set of images. To assist surveillance, this paper proposes an online scene association scheme to summarise an endoscopic video into scenes, on-the-fly. This provides scene clustering based on visual contents, and also facilitates topological localisation during navigation. The proposed method is based on tracking and detection of visual landmarks on the tissue surface. A generative model is proposed for online learning of pairwise geometrical relationships between landmarks. This enables robust detection of landmarks and scene association under tissue deformation. Detailed experimental comparison and validation have been conducted on in vivo endoscopic videos to demonstrate the practical value of our approach.
authors.forename: Menglong
authors.initials: M
authors.lastname: Ye
authors.forename: Edward
authors.initials: E
authors.lastname: Johns
authors.forename: Stamatia
authors.initials: S
authors.lastname: Giannarou
authors.forename: Guang-Zhong
authors.initials: GZ
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000033:anatomy & histology*; D053705:Capsule Endoscopes; D053704:Capsule Endoscopy / Q000379:methods*; D041981:Gastrointestinal Tract / Q000166:cytology*; 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; D013382:Subtraction Technique