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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’