title: A colon video analysis framework for polyp detection.
abstract: This paper presents an automated video analysis framework for the detection of colonic polyps in optical colonoscopy. Our proposed framework departs from previous methods in that we include spatial frame-based analysis and temporal video analysis using time-course image sequences. We also provide a video quality assessment scheme including two measures of frame quality. We extract colon-specific anatomical features from different image regions using a windowing approach for intraframe spatial analysis. Anatomical features are described using an eigentissue model. We apply a conditional random field to model interframe dependences in tissue types and handle variations in imaging conditions and modalities. We validate our method by comparing our polyp detection results to colonoscopy reports from physicians. Our method displays promising preliminary results and shows strong invariance when applied to both white light and narrow-band video. Our proposed video analysis system can provide objective diagnostic support to physicians by locating polyps during colon cancer screening exams. Furthermore, our system can be used as a cost-effective video annotation solution for the large backlog of existing colonoscopy videos.
authors.affiliation: Science and Technology InternationalMedical Systems, San Diego, CA 92037, USA. hi2sunyoung@gmail.com
authors.forename: Sun Young
authors.initials: SY
authors.lastname: Park
authors.forename: Dustin
authors.initials: D
authors.lastname: Sargent
authors.forename: Inbar
authors.initials: I
authors.lastname: Spofford
authors.forename: Kirby G
authors.initials: KG
authors.lastname: Vosburgh
authors.forename: Yousif
authors.initials: Y
authors.lastname: A-Rahim
mesh_terms: D000465:Algorithms; D003111:Colonic Polyps / Q000175:diagnosis*; D003113:Colonoscopy / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods