Classification of breast thermal images using artificial neural networks

• Autorzy: Jakubowska T. , Wiecek B. , Wysocki M. , Drews-Peszynski C. , Strzelecki M.
• Języki publikacji: EN

Abstrakty

EN

In this paper we present classification of the thermal images in order to discriminate healthy and pathological cases during breast cancer screening. Different image features and approaches for data reduction and classification have been used to distinguish healthy breast one with malignant tumour. We use image histogram and co-occurrence matrix to get thermal signatures and analyze symmetry between left and right side. The most promised method was based on wavelet transformation and nonlinear neural network classifier. The proposed approach was used in the pilot investigations in the medical centre which is permanently using thermograph for breast cancer screening, as an adjacent method for other classical diagnostic method, such as mammography.

Słowa kluczowe

EN

thermal image processing; neural network; classification

PL

przetwarzanie termogramów; sieci nuronowe; klasyfikacja

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2004
• Tom: Vol. 7
• Strony: MIP41--50
• Opis fizyczny: Bibliogr. 10 poz., rys., tab.

Twórcy

autor

Jakubowska T.

• Laser Diagnosis and Therapy Centre at Technical University of Lodz, Poland

autor

Wiecek B.

• Institute of Electronics, Technical University of Lodz, Poland

autor

Wysocki M.

• Institute of Electronics, Technical University of Lodz, Poland

autor

Drews-Peszynski C.

• Laser Diagnosis and Therapy Centre at Technical University of Lodz, Poland

autor

Strzelecki M.

• Institute of Electronics, Technical University of Lodz, Poland

Bibliografia

• [1] BENNETT M. “Breast Cancer Screening Using High-Resolution Digital Thermography”, Total Health, Vol. 22 No 6 p.44, 1985.
• [2] CAUSTON D. R., “A Biologist's Advanced Mathematics”, London, Allen and Unwin, 1987.
• [3] DEBIEC P., STRZELECKI M., MATERKA A., "Evaluation of Texture Generation Methods Based on CNN and GMRF Image Texture Models", International Conference on Signals and Electronic Systems ICSES'2000, Ustron, Oct. 2000, pp. 187-192.
• [4] JAKUBOWSKA T., WIECEK B., WYSOCKI M., DREWS-PESZYNSKI C., “Thermal Signatures for Breast Cancer Screening Comparative Study, Proc. IEEE EMBS Conf. Cancun, Mexico, Sep 17-21, 2003.
• [5] JOLLIFFE I. T., “Principal Component Analysis”. New York, Springer-Verlag, 1986.
• [6] KOCIOLEK M., MATERKA A., STRZELECKI M., SZCZYPINSKI P., "Discrete Wavelet Transform-Derived Features for Digital Image Texture Analysis", Proc. International Conference on Signals & Electronic Systems ICSES'2001, Lodz, 18-21 September 2001, pp. 111-116
• [7] MANLY B. F. J, “Multivariate Statistical Method: A Primer”. Chapman & Hall, London, 1994.
• [8] MATERKA A., STRZELECKI M., LERSKI R., SCHAD L., „Evaluation of Texture Features of Test Objects for Magnetic Resonance Imaging”, Infotech Oulu Workshop on Texture Analysis in Machine Vision, June , 1999, Oulu, Finland.
• [9] NG E.Y.K., UNG L.N., NG F.C., SIM L.S.J. “Statistical Analysis Of Healthy And Malignant Breast Thermography”, Journal of Medical Engineering & Technology, Vol. 25 No 6 (Nov/Dec 2001) p.253-263.
• [10] SCHÜRMAN J. (1996) Pattern classification, John Wiley & Sons, 1996.