title: Smoke detection in endoscopic surgery videos: a first step towards retrieval of semantic events.
abstract: BACKGROUND
  Event-based annotation of surgical operations has not received much attention, mainly due to diversity of the visual content. As a first attempt at retrieval of surgical events, we address the problem of detecting the smoke produced by electrosurgery tasks.
  
  
  METHODS
  After video decomposition into shots, a grid of particles is placed over the initial frame. The grid is advected with the space-time optical flow and a number of ad hoc kinematic features are extracted. After feature selection, a one-class support vector machine is employed for classification. A vision-based fire surveillance method is used for comparison.
  
  
  RESULTS
  Experimental evaluation is performed on individual shots and laparoscopic cholecystectomy videos. In the first set-up, average specificity and sensitivity were 86% and 83%, respectively. In video-based assessment the recognition accuracy was ≥ 80% for two of the three videos tested. The fire surveillance method had a maximum accuracy of 63%.
  
  
  CONCLUSIONS
  The irregular movement of smoke was captured robustly by the proposed features, which could also be employed for interpretation of other semantic occurrences in surgical videos.
authors.affiliation: Medical Physics Laboratory Simulation Centre, School of Medicine, University of Athens, Greece.
authors.forename: Constantinos
authors.initials: C
authors.lastname: Loukas
authors.forename: Evangelos
authors.initials: E
authors.lastname: Georgiou
mesh_terms: D000465:Algorithms; D017081:Cholecystectomy, Laparoscopic; D004598:Electrosurgery; D004724:Endoscopy; D006801:Humans; D010363:Pattern Recognition, Automated; D012906:Smoke; D060388:Support Vector Machine; D014741:Video Recording; D058067:Wavelet Analysis
chemical_list: D012906:Smoke
keywords: event detection; event retrieval; one-class support vector machine (OCSVM); smoke detection