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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References
Economou, G.P.K., Lymberopoulos, D., Karvatselou, E., Chassomeris, C.: A new concept toward computer-aided medical diagnosis – A prototype implementation addressing pulmonary diseases, IEEE Transactions on Information Technology in Biomedicine, 5 (1) 55–61 (2001)
Kovalerchuk, B., Vityaev, E., Ruiz, J.F.: Consistent knowledge discovery in medical; diagnosis, IEEE Engineering in Medicine and Biology Magazine, 19 (4) 26–37 (2000)
West, D., West, V.: Model selection for a medical diagnostic decision support system: A Breast cancer detection case, Artificial Intelligence in Medicine, 20 (3) 183–204 (2000)
Haga, Y., Esashi, M.: Biomedical microsystems for minimally invasive diagnosis and treatment, Proceedings of IEEE, 92 98–114 (2004)
Krishnan, S., Wang, P., Kugean, C., Tjoa, M.: Classification of endoscopic images based on texture and neural network, Proceedings of 23rd Annual IEEE International Conference in Engineering in Medicine and Biology, 4 3691–3695 (2001)
Maroulis, D.E., Iakovidis, D.K., Karkanis, S.A, Karras, D.A.: CoLD: A versatile detection system for colorectal lesions endoscopy video-frames, Computer Methods and Programs in Biomedicine, 70 151–166 (2003)
Idden, G., Meran, G., Glukhovsky, A., Swain, P.: Wireless capsule endoscopy, Nature, 405–417 (2000)
Gletsos, M., Mougiakakou, S., Matsopoulos, G., Nikita, K., Nikita, A., Kelekis, D.: A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier, IEEE Transactions on Information Technology in Biomedicine, 7 (3) 153–162 (2003)
Wadge, E., Boulougoura, N., Kodogiannis, V.: Computer-assisted diagnosis of wireless-capsule endoscopic images using neural network based techniques, Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA, 328–333 (2005)
Kodogiannis, V., Boulougoura, M., Wadge, E.: An intelligent system for diagnosis of capsule endoscopic images, Proceedings of the second International Conference on Computational Intelligence in Medicine and Healthcare (CIMED 2005), Portugal, 340–347 (2005)
Kodogiannis, V.: Computer-aided diagnosis in clinical endoscopy using neuro-Fuzzy system, IEEE FUZZ 2004, 1425–1429 (2004)
Boulougoura, M., Wadge, E., Kodogiannis, V., Chowdrey, H.S.: Intelligent systems for computer-assisted clinical endoscopic image analysis, Second IASTED International Conference on Biomedical Engineering, Innsbruck, Austria, 405–408 (2004)
Wadge, E., Kodogiannis, V.S.: Intelligent diagnosis of UTI in vivo using gas sensor arrays, International Conference on Neural Networks and Expert Systems in Medicine and HealthCare, NNESMED 2003, 93–98 (2003)
Kodogiannis V.S., Boulougoura M., Wadge E., Lygouras J.N.: The usage of soft-computing methodologies in interpreting capsule endoscopy, Engineering Applications in Artificial Intelligence, Elsevier, 20 539–553 (2007)
Kuncheva, L.I.: Fuzzy Classifier Design, Physica, Heidelberg (2000)
Wadge, E.: The use of EM-Based neural network schemes for modelling and classification, PhD Thesis, Westminster University, (2005)
Arena, A., Boulougoura, M., Chowdrey, H.S., Dario, P., Harendt, C., Irion, K.-M., Kodogiannis, V., Lenaerts, B., Menciassi, A., Puers, R., Scherjon, C., Turgis, D.: Intracorporeal Videoprobe (IVP), in Medical and Care Computing 2, Volume 114 Studies in Health Technology and Informatics, Edited by: Bos, L. Laxminarayan, S. and Marsh, A. IOS Press, Amsterdam, Netherlands, 167–174 (2005)
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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
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