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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Kodogiannis, Vassilis
Publisher: IEEE Computer Society
Languages: English
Types: Part of book or chapter of book
Subjects: UOW3

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
In this paper, an innovative detection system to\ud support medical diagnosis and detection of abnormal lesions\ud by processing endoscopic images is presented. The images\ud used in this study have been obtained using the new M2A\ud Swallowable Imaging Capsule - a patented, video colourimaging disposable capsule. Schemes have been developed to extract new texture features from the texture spectra in the hromatic and achromatic domains for a selected region of nterest from each colour component histogram of endoscopic images. The implementation of an advanced fuzzy inference neural network which combines fuzzy systems and artificial neural networks and the concept of fusion of multiple classifiers dedicated to specific feature parameters have been also adopted in this paper. The detection accuracy of the proposed system has reached to loo%, providing thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in endoscopy.\ud
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Theofanous, “Tumor recognition in endoscopic video images”, 26'' EUROMICRO conf. Netherlands, pp. 423429,2000.
    • [2] S. Krishnan, P. Wang, C. Kugean, M. Tjoa, “Classification of endoscopic images based on texturc and neural network”, Proc. 23rd Annual IEEE Inr. Con$ in Engineering in Medicine and Biology, Vol. 4, pp. 3691- 3695,2001.
    • S. Krishnan, X. Yang, K. Chan, S. Kumar, P. Goh, “Intestinal abnormality detection from endoscopic images”, Inr. conf: ofthe IEEE on Engineeringin Medicine and Biology Socieiy, Vol. 2, pp. 895-898, 1998.
    • D.E. Maroulis, D.K. Iakovidis, S.A. Karkanis, D.A. Karras, “COLD: a versatile detection system for colorectal lesions endoscopy video-frames”, Computer Methurls and Program in Biomedicine, Vol. 70, pp. 151-166, 2003.
    • R.M. Haralick, “Statistical and structural approaches to texture”, IEEE Proc., Vol. 67, pp. 786- 804, 1979 M. Boulougoura, E. Wadge, V.S. Kodogiannis, H.S. Chowdrey, “Intelligent systems for computer-assisted clinical endoscopic image analysis”, 2”' IASTED Int. Cone on BIOMEDICAL ENGINEERING, Innsbruck, Austria, pp. 405-408,2004.
    • E. Wadge, V. Kodogiannis, D. Tomtsis, “Neuro-Fuzzy Ellipsoid Basis Function multiplc classifier for diagnosis of urinary Tract Infections”, Proc. ICCMSE 2003, Greece, pp. 673-677,2003 V. Kodogiannis, “An efficient fuzzy based technique for signal classification”, Journal of Intelligenr & Fuzzy Systems, Vol. 11, No. %, pp.
    • L.I. Kuncheva, Fuzz?,ClassffierDesign, Physica-Verlag, 2000
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