OBJECTIVES: 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.
OBJECTIVES: 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: Thomas M Deserno Né Lehmann; Heinz Handels; Klaus H Maier-Hein Né Fritzsche; Sven Mersmann; Christoph Palm; Thomas Tolxdorff; Gudrun Wagenknecht; Thomas Wittenberg Journal: Curr Med Imaging Rev Date: 2013-05