IMAGE ANALYSIS METHODS – ANALYSIS OF MAMMOGRAPHIC IMAGE BASED ON TEXTURAL FEATURES
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
mammography; medical diagnostic imaging; image texture analysis; image classification
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.
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.
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.
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.
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.
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.
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.
Szczepaniak P.S.: Obliczenia inteligentne, szybkie przekształcenia i klasyfikatory, Akademica Oficyna Wydawnicza EXIT, Warszawa, 2004.
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.
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.
„Mias dataset”. http://peipa.essex.ac.uk/info/mias.html. MIAS dataset.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.