Fuzzy algorithm for madical image processing

• Autorzy: Śrutek M. , Śrutek E.
• Języki publikacji: EN

Abstrakty

EN

Fuzzy rules were used to enhance images in the pre-processing step. The contrast enhancement and filtration methods are proposed to eliminate noise and artifacts from images. Implementation of fuzzy rules resulted in considerable image quality improvement. Next, the segmentation of regions corresponding to abnormalities is performed and geometrical and statistical parameters of the detected structures are calculated. Final classification algorithm is based on fuzzy rules.

Słowa kluczowe

EN

image processing; fuzzy processing; microcalcifications; mammography; medical image

PL

przetwarzanie obrazu; przetwarzanie rozmyte; mammografia; obraz medyczny

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2006
• Tom: Vol. 11, no 1
• Strony: 115--122
• Opis fizyczny: Bibliogr. 12 poz., rys., wykr.

Twórcy

autor

Śrutek M.

autor

Śrutek E.

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

Bibliografia

• [1] Carvalho B.M. et al., Algorithms for Fuzzy Segmentation, Pattern Analysis & Applications, pp. 73-81, 1999,
• [2] Cheng H.D. et al., A novel fuzzy logic approach to microcalcification detection, Information Sciences, pp. 189-205, 1998;
• [3] Dziukowa J., Mammografia w diagnostyce raka sutka, BEL CORP Sciencific Publications, W-wa 1998,
• [4] Gulsrud T. 0., Texture Analisys of Digital Mammograms. dissertation, Hogskolen & Stavanger, 2000,
• [5] Iwaszkiewicz K., Atlas mammografii, PZWL, W-wa 1993,
• [6] Kowski R,., Kubasiewicz M., Mammografia. Podręcznik zachowania standardów jakości, Wydawnictwo Lekarskie PZWL, Warszawa 2001,
• [7] Mancuso M., De Luca R., Poluzzi R., Rizzotto G.G., A fuzzy decision filter for impulse noise reduction, Fuzzy Sets and Systems, vol. 77, pp. 111-116, 1996,
• [8] Mendel A.J. et al., Automatic detection of breast border and nipple in digital mammograms. Computer Methods and Programs in Biomedicine, vol. 49, pp. 253-262, 1996
• [9] Rick A. et al., Quantitative Modelling of Microcalcification Detection in Digital Mammography, GE Medical Systems, pp. 32-41, 2000,
• [10] Śrutek M., Top Point Method for Microcacifications Detection in Mammography Images, Image Processing & Communication, vol. 10, pp. 131-141, 2005,
• [11] Vitulano S., Di Ruberto C., Nappi M., Different methods to segment biomedical images, Pattern Recognition Letters, vol. 18, pp. 1125-1131, 1997,
• [12] http://www.wiau.man.ac.uk/services /mias/MIASweb.html