Table 1.

Texture Parameters

Texture features	Formula or assumption
1. Histogram-based features	Assumption) p(i) is a normalized histogram value for a pixel intensity I. μ  is mean value.
Variance (σ 2)	${\mathit{\sigma }}^2={\displaystyle \sum _{\mathit{i}=1}^{\mathit{N}}}{\left(\mathit{i}-\mathit{\mu }\right)}^2\mathit{p}\left(\mathit{i}\right)$
2. Absolute gradient-based parameters	Assumption) x(i,j) is a pixel intensity at (i,j) position
Absolute gradient value [G(i,j)] for 5 × 5 matrix of M elements	$\mathit{G}\left(\mathit{i},\mathit{j}\right)=\sqrt{{\left({\mathit{x}}_{\mathit{i}+2,\mathit{j}}-{\mathit{x}}_{\mathit{i}-2,\mathit{j}}\right)}^2+{\left({\mathit{x}}_{\mathit{i},\mathit{j}+2}-{\mathit{x}}_{\mathit{i},\mathit{j}-2}\right)}^2}$
Mean of G(i,j), (GrMean)	$\mathit{GrMean}=\frac{1}{\mathit{M}}{\displaystyle \sum _{\mathit{i},\mathit{j}\in \mathit{ROI}}}\mathit{G}\left(\mathit{i},\mathit{j}\right)$
Variance of G(i,j), (GrVariance)	$\mathit{GrVariance}=\frac{1}{\mathit{M}}{\displaystyle \sum _{\mathit{i},\mathit{j}\in \mathit{ROI}}}{\left(\mathit{G}\left(\mathit{i},\mathit{j}\right)-\mathit{GrMean}\right)}^2$
Ratio of non-zero G(i,j) matrix elements, (GrNonZeros)	Ratio of non-zero G(i,j) values
3. Run-length matrix-based parameters a
4. Co-occurrence matrix-based parameters	Assumption) p(i,j) is the joint probability of co-occurring pixel intensity values i and j. N x is a number of pixel intensities. $\begin{array}{cc}\hfill {\mathit{p}}_{\mathit{x}+\mathit{y}}\left(\mathit{k}\right)={\displaystyle \sum _{\mathit{i}=1}^{{\mathit{N}}_{\mathit{x}}}}{\displaystyle \sum _{\mathit{j}=1}^{{\mathit{N}}_{\mathit{x}}}}\mathit{p}\left(\mathit{i},\mathit{j}\right),\hfill & \hfill \mathit{i}+\mathit{j}=\mathit{k}\hfill \end{array}$ ${\mathit{p}}_{\mathit{x}-\mathit{y}}\left(\mathit{k}\right)={\displaystyle \sum _{\mathit{i}=1}^{{\mathit{N}}_{\mathit{x}}}}{\displaystyle \sum _{\mathit{j}=1}^{{\mathit{N}}_{\mathit{x}}}}\mathit{p}\left(\mathit{i},\mathit{j}\right),|\cdots \mathit{i}-\mathit{j}|=\mathit{k}$
Angular second moment (AngScMom)	$\mathit{AngScMom}={\displaystyle \sum _{\mathit{i}=1}^{{\mathit{N}}_{\mathit{x}}}}{\displaystyle \sum _{\mathit{j}=1}^{{\mathit{N}}_{\mathit{x}}}}\mathit{p}{\left(\mathit{i},\mathit{j}\right)}^2$
Entropy	$\mathit{Entropy}=-{\displaystyle \sum _{\mathit{i}=1}^{{\mathit{N}}_{\mathit{x}}}}{\displaystyle \sum _{\mathit{j}=1}^{{\mathit{N}}_{\mathit{x}}}}\mathit{p}\left(\mathit{i},\mathit{j}\right)log\left(\mathit{p}\left(\mathit{i},\mathit{j}\right)\right)$
Inverse difference moment (InvDfMom)	$\mathit{InvDfMom}={\displaystyle \sum _{\mathit{i}=1}^{{\mathit{N}}_{\mathit{x}}}}{\displaystyle \sum _{\mathit{j}=1}^{{\mathit{N}}_{\mathit{x}}}}\frac{1}{1+{\left(\mathit{i}-\mathit{j}\right)}^2}\mathit{p}\left(\mathit{i},\mathit{j}\right)$
Sum of squares (SumOfSqs)	$\mathit{SumOfSqs}={\displaystyle \sum _{\mathit{i}=1}^{{\mathit{N}}_{\mathit{x}}}}{\displaystyle \sum _{\mathit{j}=1}^{{\mathit{N}}_{\mathit{x}}}}{\left(\mathit{i}-{\mathit{\mu }}_{\mathit{x}}\right)}^2\mathit{p}\left(\mathit{i},\mathit{j}\right)$
Sum entropy (SumEntrp)	$\mathit{SumEntrp}=-{\displaystyle \sum _{\mathit{i}=1}^{2{\mathit{N}}_{\mathit{x}}}}{\mathit{p}}_{\mathit{x}+\mathit{y}}\left(\mathit{i}\right)log\left({\mathit{p}}_{\mathit{x}+\mathit{y}}\left(\mathit{i}\right)\right)$
Sum average (SumAverg)	$\mathit{SumAverg}={\displaystyle \sum _{\mathit{i}=1}^{2{\mathit{N}}_{\mathit{x}}}}\mathit{i}{\mathit{p}}_{\mathit{x}+\mathit{y}}\left(\mathit{i}\right)$
Sum variance (SumVarnc)	$\mathit{SumVarnc}={\displaystyle \sum _{\mathit{i}=1}^{2{\mathit{N}}_{\mathit{x}}}}{\left(\mathit{i}-\mathit{SumAverg}\right)}^2{\mathit{p}}_{\mathit{x}+\mathit{y}}\left(\mathit{i}\right)$
Difference entropy (DifEntrp)	$\mathit{DifEntrp}=-{\displaystyle \sum _{\mathit{i}=1}^{{\mathit{N}}_{\mathit{x}}}}{\mathit{p}}_{\mathit{x}-\mathit{y}}\left(\mathit{i}\right)log\left({\mathit{p}}_{\mathit{x}-\mathit{y}}\left(\mathit{i}\right)\right)$
Difference variance (DifVarnc)	$\mathit{DifVarnc}={\displaystyle \sum _{\mathit{i}=0}^{{\mathit{N}}_{\mathit{x}}-1}}{\left(\mathit{i}-{\mathit{\mu }}_{\mathit{x}-\mathit{y}}\right)}^2{\mathit{p}}_{\mathit{x}-\mathit{y}}\left(\mathit{i}\right)$
5. Autoregressive model-based parameters a

^a No texture feature with significant value in this study; cf. Table 3.