<![CDATA[We have studied the effectiveness of using texture features derived from gray-level co-occurrence matrix(GLCM) matrices for classification of cystic mass and non-cystic mass in ultra sonograms. Twenty-three (23)region of interest (ROIs) containing cystic masses and fifty-five (55) non-cystic masses were extracted from ultrasonogram for this study. For each ROI of 50x50 pixels, seven features (energy, inertia, entropy, homogeneity,maximum probability, inverse difference moment, and correlation) were calculated. The importance of eachfeature in distinguishing cystic masses from non-cystic masses was determined by linear discriminant analysiswith SPSS version 11.5 program. As a result of a study, it was found that all seven features can distinguishingcystic masses from non-cystic masses with an accuracy about 91 %-92.3%. Those levels of accuracy also foundwhen two features (energy and inverse difference moment) was excluded from analysis. The result demonstratethe feasibility of using texture features based on GLCM for distinguishing cystic masses from non-cystic massesof ultra sonogram .Keywords: Gray-level Co-occurrence Matrix Ultrasonografi, massa kistik, fitur tekstur, analisis tekstur, analisisdiskriminan]]>
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