Neural network-based approach for the classification of wireless-capsule endoscopic images

Kodogiannis, Vassilis and Boulougoura, Maria (2005) Neural network-based approach for the classification of wireless-capsule endoscopic images. In: 2005 IEEE International Joint Conference on Neural Networks (IJCNN). IEEE Computer Society, USA, pp. 2423-2428. ISBN 0780390482

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Official URL: http://dx.doi.org/10.1109/IJCNN.2005.1556282

Abstract

The importance of computer-assisted diagnosis in endoscopy is to assist the physician in detecting the status of tissues by characterising the features from the endoscopic image. In this paper schemes have been developed to extract new 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 Imaging Capsule. The concept of fusion of multiple classifiers dedicated to specific feature parameters and the implementation of an advanced intelligent scheme have been also adopted in this study. The high detection accuracy of the proposed systems provides thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in capsule endoscopy.

Item Type: Book Section
Subjects: University of Westminster > Science and Technology > Electronics and Computer Science, School of (No longer in use)
Depositing User: Miss Nina Watts
Date Deposited: 26 Jun 2006
Last Modified: 11 Aug 2010 14:30
URI: http://westminsterresearch.wmin.ac.uk/id/eprint/2186

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