ROC curves and video analysis optimization in intestinal capsule endoscopy

Authors:
Fernando Vilariño;Ludmila I. Kuncheva;Petia Radeva
Affiliations:
Computer Vision Centre, Universitat Autonoma de Barcelona, 08193 Bellaterra, Barcelona, Spain;School of Informatics, University of Wales, Bangor LL57 1UT, UK;Computer Vision Centre, Universitat Autonoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
Venue:
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Year:
2006

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Abstract

Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions.