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Table 2 Features extracted using different texture models

From: Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor

Model Extracted features
SGLCM [F1-F14] ‘ASM’, ‘Contrast’, ‘Correlation’, ‘Sum_Squares’, ‘Inverse_Diff_Moment’, ‘Sum_Average’, ‘Sum_Variance’, ‘Sum_Entropy’, ‘Entropy’, ‘Diff_Variance’, ‘Diff_Entropy’, ‘Info_Measure1’, ‘Info_Measure2’, ‘Max_Corr_Coff’
Gray level difference statistics (GLDS) [F15-F19] ‘Homogeneity’, ‘Contrast’, ‘Mean’, ‘Energy’, ‘Entropy’
First order statistical (FOS) [F20-F23] ‘Mean’, ‘Variance’, ‘Skewness’, ‘Kurtosis’
Statistical feature matrix (SFM) [F24-F27] ‘Mean’, ‘Variance’, ‘Skewness’, ‘Kurtosis’
Law’s texture energy measures (LTEM) [F28-F41] ‘EE’, ‘SS’, ‘WW’, ‘RR’, ‘EL’, ‘SL’, ‘WL’, ‘RL’, ‘SE’, ‘WE’, ‘RE’, ‘WS’, ‘RS’, ‘RW’
Fractal [F42-F43] ‘H1’, ‘H2’
Fourier power spectrum (FPS) [F44-F45] ‘Sr’, ‘Stheta’