Detecting abnormalities in capsule endoscopic images by textural description and neural networks

Authors:
V. S. Kodogiannis;E. Wadge;M. Boulougoura;K. Christou
Affiliations:
Mechatronics Group, School of Computer Science, University of Westminster, London, UK;Mechatronics Group, School of Computer Science, University of Westminster, London, UK;Mechatronics Group, School of Computer Science, University of Westminster, London, UK;Mechatronics Group, School of Computer Science, University of Westminster, London, UK
Venue:
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Year:
2005

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Neuro-fuzzy ellipsoid basis function multiple classifier for diagnosis of urinary tract infections

ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
Fuzzy Classifier Design

Fuzzy Classifier Design

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Abstract

In this paper, a detection system to support medical diagnosis and detection of abnormal lesions by processing endoscopic images is presented. The endoscopic images possess rich information expressed by texture. Schemes have been developed to extract texture features from the texture spectra in the chromatic and achromatic domains for a selected region of interest from each colour component histogram of images acquired by the new M2A Swallowable Capsule. The implementation of an advanced neural network scheme and the concept of fusion of multiple classifiers have been also adopted in this paper. The preliminary test results support the feasibility of the proposed method.