A neuro-fuzzy-based system for detecting abnormal patterns in wireless-capsule endoscopic images

Authors:
V. S. Kodogiannis;M. Boulougoura;J. N. Lygouras;I. Petrounias
Affiliations:
Centre for Systems Analysis, School of Computer Science, University of Westminster, London HA1 3TP, UK;Development, Innovations and Projects SWC Communication Systems, SIEMENS S.A., Athens GR-14564, Greece;Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi GR-67100, Greece;School of Informatics, University of Manchester, Manchester M60 1QD, UK
Venue:
Neurocomputing
Year:
2007

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Abstract

Wireless capsule endoscopy (WCE) constitutes a recent technology in which a capsule with micro-camera attached to it, is swallowed by the patient. This paper presents an integrated methodology for detecting abnormal patterns in WCE images. Two issues are being addressed, including the extraction of texture features from the texture spectra in the chromatic and achromatic domains from each colour component histogram of WCE images and the concept of a fusion of multiple classifiers. The implementation of an advanced neuro-fuzzy learning scheme has been also adopted in this paper. The high detection accuracy of the proposed system provides thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in WCE.