Top points method for microcalcifications detection in mammography images

• Autorzy: Śrutek M.
• Języki publikacji: EN

Abstrakty

EN

Novel method of microcalcifications detection in mammography images is proposed in the paper. The core of the method is the top points localization algorithm, in which the brightest points within the 4-element neighbourhood are detected iteratively. For points detected in such manner, the rules based on the mean and median values of the points neighbourhood are created. Moreover, the histogram's parameters are taken into account in order to choose top points that correspond to microcalficications. Next the region growing of the top points' area is performed. All the neighbouring points that fulfil conditions regarding the illumination level difference and mean illuminance of the extracted region, are also added to the microcalficication region. For microcalcifications extracted accordingly with the proposed algorithm the values of geometrical and statistical parameters can be computed.

Słowa kluczowe

EN

image processing; top points method; microcalcifications; mammography

PL

przetwarzanie obrazów; mammografia

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2005
• Tom: Vol. 10, no 2
• Strony: 131--142
• Opis fizyczny: Bibliogr. 16 poz., rys., wykr.

Twórcy

autor

Śrutek M.

• Institute of Telecommunication, ATR Bydgoszcz, Poland Kaliskicgo 7, 85-791 Bydgoszcz,

Bibliografia

• [1] Chen C.H., Lee G.G., On Digital Mammogram Segmentation and Microcalcification Detection Using Multiresolution Wavelet Analysis, Graphics Model and Image Processing, vol. 59, No. 5, 1997, pp. 349-364,
• [2] Cheng H.D., et al., A novel fuzzy logic approach to microcalcification detection, Journal of Information Sciences 111, 1998, pp. 189-205,
• [3] Cheng H.D., Wang J., Shi X., Microcacification detection using fuzzy logic and scale space approaches, Pattern Recognition 37, 2004, pp. 363-375,
• [4] Choraś R.S., Śrutek M., Przetwarzanie obrazów mammograficznych celem wydzielenia parametrów zmian z wykorzystaniem logiki rozmytej, materiały konferencyjne, XIV Krajowa Konferencja Naukowa "Biocybernetyka i Inżynieria Biomedyczna", 2005, pp. 243-249,
• [5] Chrzan R. et al., Digital mammography in the evaluation of clusters of breast microcalcifications without accompanying masses, Polish Journal of Radiology 67, 2002, pp. 49-58,
• [6] Ferreira C.B.R., Borges D. L., Analysis of mammogram classification using a wavelet transform decomposition, Pattern Recognition Letters 24, 2003, pp. 973-982,
• [7] http://www.wiau.man.ac.uk/services/mias/MIASweb.html
• [8] Mercer R. E., Barron J. L., Bruen A. A., Cheng D., Fuzzy points: algebra and application, Pattern Recognition 35, pp. 1153-1166, 2002,
• [9] Nałęcz M., Biocybernetyka i inżynieria biomedyczna 2000, tom 8, Detekcja mikrozwapnień w mammogramach z zastosowaniem rekonstrukcji, EXIT, 2003, pp. 100-104,
• [10] Nałęcz M., Biocybernetyka i inżynieria biomedyczna 2000, torn.8, Obrazowanie biomedyczne, EXIT, pp. 165-177, 2003,
• [11] Sehad S., Desarnaud S. Strauss A., Artificial neural classification of clustered microcalcifications on digitized mammograms, Proceedings of the IEEE Inernacional Conference on Systems, Man and Cybernetics, vol. 5, 1997, pp. 1273-1275
• [12] Śrutek M., Analiza obrazów z wykorzystaniem zbiorów rozmytych w teleinformatycznym systemie diagnostyki medycznej, Ph.D. thesis, ATR Bydgoszcz 2005,
• [13] Ustymowicz M., Nieniewski M., Morphological method of microcalcifications detection in mammograms, International Conference on Computer Graphics and Vision, Kluwer, 2004
• [14] Woods K., et al., A neural network approach to microcalcifications detection, IEEE Nuclear Science Symposium and Medical Imaging Conference, 1992, pp. 1273-1275,
• [15] Yu S., Brown S., Xue Y., Guan L., Enhancement and identification of microcalcifications in mammograms images using wavelets, Proceedings Man and Cybernetics IEEE'96, pp. 1166-1171, 1996,
• [16] Zheng B., Qian L.P., Clarke L.P., Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications, IEEE Transaction on Medical Images 15 (5), 1996, pp. 589-597,