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Table 1 Texture features at full resolution

From: Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions

TA Method Greylevel RGB HSI
COM Sum of Squares G_ Sum of Squares H_ Sum Variance
  Sum Variance R_ Sum of Squares H_Correlation
  Sum Entropy G_ Sum Variance H_Inverse Difference Moment
RLM Horizontal greylevel non-uniformity G_ Horizontal greylevel non-uniformity I_ Horizontal Run length non-uniformity
  Vertical greylevel non-uniformity G_45° greylevel non-uniformity I _Horizontal Fraction
  135°greylevel non-uniformity G_135° greylevel non-uniformity I _135° Run length non-uniformity
WT E 4 G_E 4 I _E 4
  E 5 G_E 5 S_E 4
  E 2 B_E 4 I _E 4
  1. The texture features (parameters with the highest F-Coefficient) that discriminate between the C and F groups on greylevel-, RGB-, and HSI- schemes at full-resolution images, using TA methods:COM, RLM, and WT.
  2. R_: Red, G_: Green, and B_: Blue channels. H_: Hue, S_: Saturation, and I _: Intensity. E s Energy calculated from the wavelets using various scales (s).