IMAGE ANALYSIS METHODS – ANALYSIS OF MAMMOGRAPHIC IMAGE BASED ON TEXTURAL FEATURES
Jagoda Lazarek
jagoda.lazarek@p.lodz.plPolitechnika Łódzka, Wydział Fizyki Technicznej, Informatyki i Matematyki Stosowanej, Instytut Informatyki (Poland)
Abstract
This paper presents an analysis of the possibility of using textural features for mammographic images classification. Textural features are calculated base on histogram, gradient matrix, run-length matrix, co-occurence matrix. Classification is based on k-NN classifier, the regions of interest can be classified as normal or abnormal. Results of some experiments are presented. All of abnormal regions were classified correctly
Keywords:
mammography, medical diagnostic imaging, image texture analysis, image classificationReferences
Guliato D., Rangayyan R., Carnielli W., Zuo J., Desautels J.: Segmentation of breast tumors in mammograms by fuzzy region growing, Engineering in Medicine and Biology Society, Proceedings of the 20th Annual International Conference of the IEEE, 1998, vol. 2, pp. 1002-1005.
Google Scholar
Haralick R.M., Shanmugam K., Dinstein I.: Textural Features for Image Classification, IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-3, 1973, No. 6, pp. 610-621.
DOI: https://doi.org/10.1109/TSMC.1973.4309314
Google Scholar
Huo Z., Giger M., Vyborny C., Metz C.: Breast cancer: Effectiveness of computer-aided diagnosis – observer study with independent database of mammograms1, Radiology, 2002, vol. 224, no. 2, pp. 560-568.
DOI: https://doi.org/10.1148/radiol.2242010703
Google Scholar
Lazarek J., Szczepaniak P.S., Tomczyk A.: Method of Pattern Detection in Mammographic Images, Intelligent Systems in Technical and Medical Diagnosis, Eds. Józef Korbicz, Marek Kowal. Springer, 2014, pp. 235-245.
DOI: https://doi.org/10.1007/978-3-642-39881-0_19
Google Scholar
Lyra M., Lyra S., Kostakis B., Drosos S., Georgosopoulus C., Skouroliakou K.: Digital mammography texture analysis by computer assisted image processing, IEEE International Workshop on Imaging Systems and Techniques – IST 2008 Chania, Greece, September 10–12, 2008.
DOI: https://doi.org/10.1109/IST.2008.4659944
Google Scholar
Rangayyan R. M., Ayres F. J., Desautels J. L.: A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs, Journal of the Franklin Institute, vol. 344, no. 34, 2007 pp. 312-348.
DOI: https://doi.org/10.1016/j.jfranklin.2006.09.003
Google Scholar
Sampat M., Markey M., Bovik A.: Computer-aided detection and diagnosis in mammography, Handbook of Image and Video Processing, vol. 2, 2005, pp. 1195-1217.
DOI: https://doi.org/10.1016/B978-012119792-6/50130-3
Google Scholar
Szczepaniak P.S.: Obliczenia inteligentne, szybkie przekształcenia i klasyfikatory, Akademica Oficyna Wydawnicza EXIT, Warszawa, 2004.
Google Scholar
Szczepaniak P.S., Tadeusiewicz R.: The role of artificial intelligence, knowledge and wisdom in automatic image understanding, Journal of Applied Computer Science – JACS, 18, 2010, No.1, pp. 75-85.
Google Scholar
Zhou J., Feng Ch., Liu X., Tang J.: A Texture Features based Medical Image Retrieval System for Breast Cancer, 2012 7th International Conference on Computing and Convergence Technology (ICCCT), IEEE, pp. 1010 – 1015.
Google Scholar
„Mias dataset”. http://peipa.essex.ac.uk/info/mias.html. MIAS dataset.
Google Scholar
Authors
Jagoda Lazarekjagoda.lazarek@p.lodz.pl
Politechnika Łódzka, Wydział Fizyki Technicznej, Informatyki i Matematyki Stosowanej, Instytut Informatyki Poland
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