title: Automatic lesion detection in capsule endoscopy based on color saliency: closer to an essential adjunct for reviewing software.
abstract: BACKGROUND
  The advent of wireless capsule endoscopy (WCE) has revolutionized the diagnostic approach to small-bowel disease. However, the task of reviewing WCE video sequences is laborious and time-consuming; software tools offering automated video analysis would enable a timelier and potentially a more accurate diagnosis.
  
  
  OBJECTIVE
  To assess the validity of innovative, automatic lesion-detection software in WCE.
  
  
  METHODS
  A color feature-based pattern recognition methodology was devised and applied to the aforementioned image group.
  
  
  METHODS
  This study was performed at the Royal Infirmary of Edinburgh, United Kingdom, and the Technological Educational Institute of Central Greece, Lamia, Greece.
  
  
  METHODS
  A total of 137 deidentified WCE single images, 77 showing pathology and 60 normal images.
  
  
  RESULTS
  The proposed methodology, unlike state-of-the-art approaches, is capable of detecting several different types of lesions. The average performance, in terms of the area under the receiver-operating characteristic curve, reached 89.2 ± 0.9%. The best average performance was obtained for angiectasias (97.5 ± 2.4%) and nodular lymphangiectasias (96.3 ± 3.6%).
  
  
  CONCLUSIONS
  Single expert for annotation of pathologies, single type of WCE model, use of single images instead of entire WCE videos.
  
  
  CONCLUSIONS
  A simple, yet effective, approach allowing automatic detection of all types of abnormalities in capsule endoscopy is presented. Based on color pattern recognition, it outperforms previous state-of-the-art approaches. Moreover, it is robust in the presence of luminal contents and is capable of detecting even very small lesions.
authors.affiliation: Department of Computer Engineering, Technological Educational Institute of Central Greece, Lamia, Greece.
authors.forename: Dimitris K
authors.initials: DK
authors.lastname: Iakovidis
authors.affiliation: Endoscopy Unit, The Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Anastasios
authors.initials: A
authors.lastname: Koulaouzidis
mesh_terms: D053704:Capsule Endoscopy / Q000379:methods*; D016022:Case-Control Studies; D003116:Color; D003936:Diagnosis, Computer-Assisted; D004378:Duodenal Diseases / Q000175:diagnosis*; D001330:Electronic Data Processing; D006471:Gastrointestinal Hemorrhage / Q000175:diagnosis; D006801:Humans; D007077:Ileal Diseases / Q000175:diagnosis*; D007091:Image Processing, Computer-Assisted; D007417:Intestinal Polyps / Q000175:diagnosis; D007579:Jejunal Diseases / Q000175:diagnosis*; D008201:Lymphangiectasis, Intestinal / Q000175:diagnosis; D010363:Pattern Recognition, Automated; D010437:Peptic Ulcer / Q000175:diagnosis; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software; D013281:Stomatitis, Aphthous / Q000175:diagnosis