Summary
In this chapter, 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 Swallow-able Capsule. The implementation of advanced neural learning-based schemes and the concept of fusion of multiple classifiers dedicated to specific feature parameters have been also adopted in this chapter. The test results support the feasibility of the proposed methodology.
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Kodogiannis, V.S., Lygouras, J.N., Pachidis, T. (2008). An Intelligent Decision Support System in Wireless-Capsule Endoscopy. In: Chountas, P., Petrounias, I., Kacprzyk, J. (eds) Intelligent Techniques and Tools for Novel System Architectures. Studies in Computational Intelligence, vol 109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77623-9_15
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DOI: https://doi.org/10.1007/978-3-540-77623-9_15
Publisher Name: Springer, Berlin, Heidelberg
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