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 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 Capsule. The implementation of an advanced fuzzy inference neural network which combines fuzzy systems and clustering schemes and the concept of fusion of multiple classifiers dedicated to specific feature parameters have been also adopted in this paper. The preliminary test results support the feasibility of the proposed method.
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References
Idden, G., Meran, G., Glukhovsky, A., Swain, P.: Wireless capsule endoscopy. Nature, 405–417 (2000)
Haralick, R.M.: Statistical and structural approaches to texture. IEEE Proc. 67, 786–804 (1979)
Boulougoura, M., Wadge, E., Kodogiannis, V., Chowdrey, H.S.: Intelligent systems for computer-assisted clinical endoscopic image analysis. In: 2nd IASTED Int. Conf. on Biomedical Engineering, Innsbruck, Austria, pp. 405–408 (2004)
Kuncheva, L.I.: Fuzzy Classifier Design. Physica-Verlag, Heidelberg (2000)
Wadge, E., Kodogiannis, V.S.: Intelligent diagnosis of UTI in vivo using gas sensor arrays. In: Int. Conf. on Neural Networks and Expert Systems in Medicine and HealthCare, NNESMED 2003, pp. 93–98 (2003)
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Kodogiannis, V., Chowdrey, H.S. (2005). A Neurofuzzy Methodology for the Diagnosis of Wireless-Capsule Endoscopic Images. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_100
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DOI: https://doi.org/10.1007/11550822_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28752-0
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