title: Computer-assisted diagnosis for precancerous lesions in the esophagus.
abstract: OBJECTIVE
  The interpretation of endoscopic findings by gastroenterologists is still a difficult and highly subjective task. Despite important developments such as chromo-endoscopy, pit pattern analysis, fluorescence imaging as well as narrow band imaging it still requires lots of experience and training with a certain tentativeness until the final biopsy. By the development of computer-assisted diagnosis (CAD) systems this process can be supported.
  
  
  METHODS
  This paper presents a new approach to CAD for precancerous lesions in the esophagus based on color-texture analysis in a content-based image retrieval (CBIR) framework. The novelty of our approach lies in the combination of newly developed color-texture features with the interactive feedback loop provided by a relevance feedback algorithm. This allows the expert to steer the query and is still robust against accidental false decisions.
  
  
  RESULTS
  We reached an inter-rater reliability of kappa = 0.71 on a database of 390 endoscopic images. The retrieval accuracy didn't change significantly until a wrong decision rate of 20%.
  
  
  CONCLUSIONS
  Thus, the system could be able to support practitioners with less experience or in private practice. In combination with a connected case database it can also support case-based reasoning for the diagnostic decision process.
authors: Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany. christian.muenzenmayer@iis.fraunhofer.de
authors: C
authors: C
authors: Münzenmayer
authors: A
authors: A
authors: Kage
authors: T
authors: T
authors: Wittenberg
authors: S
authors: S
authors: Mühldorfer
mesh_terms: D001471:Barrett Esophagus / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D004938:Esophageal Neoplasms / Q000175:diagnosis*; D004945:Esophagoscopy; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D011230:Precancerous Conditions / Q000175:diagnosis*