## Materials and Methods
### Feature Extraction
#### 2.4.3 Run-Length Matrix Features  
[PERSON] [28] introduced the gray-level run-length matrix (GLRM) in run-length (RL) features to explain the length of the run. Because it is a constant, the number of pixels in the collinear is measured horizontally in a particular direction (0, 45, 90, and 135\({}^{\circ}\)). According to [PERSON], a section of gray scale or color level--also known as range or length of run--is a linear multitude of continuous pixels with the same color or gray level in a particular direction [29].  
Let \(\kappa_{g}\) be the number of discreet intensity values in the image, \(K_{r}\) be the number of discreet run lengths in the image, \(K_{p}\) be the number of voxels in the image, \(K_{r}(\mu)\) be the number of runs in the image along angle \(\mu\) and \(\psi(x,y|\mu)\) be the run-length matrix for an arbitrary direction \(\theta\). Then, the short run emphasis (SRE) is described in Equation (18).  
\[SRE=\frac{\sum_{x=0}^{\kappa_{g}}\sum_{y=1}^{K_{r}}\frac{\psi(x,y|\mu)}{y^{2}} }{\kappa_{r}(\mu)} \tag{18}\]  
SRE is a measure of the distribution of short run lengths, with a greater value indicative of shorter run lengths and finer textural textures. Long run emphasis (LRE) is described in Equation (19).  
\[LRE=\frac{\sum_{x=1}^{K_{g}}\sum_{y=0}^{K_{r}}\psi(x,y|\mu)y^{2}}{K_{r}(\mu)} \tag{19}\]  
LRE is a measure of the distribution of long run lengths, with a greater value indicative of longer run lengths and more coarse structural textures. Gray-level nonuniformity (GLN) is described in Equation (20).  
\[GLN=\frac{\sum_{x=1}^{K_{g}}\left[\sum_{y=1}^{K_{r}}\psi(x,y|\mu)\right]^{2}} {\kappa_{r}(\mu)} \tag{20}\]GLN measures the similarity of gray-level intensity values in the image, where a lower GLN value correlates with a greater similarity in intensity values. Gray-level nonuniformity normalized (GLNN) is defined in Equation (21).  
\[GLNN=\frac{\sum_{x=1}^{x_{g}}\left[\sum_{y=1}^{x_{g}}\psi(x,y\big{|}\mu)\right]^ {2}}{\kappa_{r}(\mu)^{2}} \tag{21}\]  
GLNN measures the similarity of gray-level intensity values in the image, where a lower GLNN value correlates with a greater similarity in intensity values. This is the normalized version of the GLN formula. Run-length nonuniformity (RLN) is described in Equation (22).  
\[RLN=\frac{\sum_{y=1}^{x_{r}}\left[\sum_{x=1}^{x_{g}}\psi(x,y\big{|}\mu)\right] ^{2}}{\kappa_{r}(\mu)} \tag{22}\]  
RLN measures the similarity of run lengths throughout the image, with a lower value indicating more homogeneity among run lengths in the image. Run-length nonuniformity normalized (RLNN) is defined in Equation (23).  
\[RLNN=\frac{\sum_{y=1}^{x_{r}}\left[\sum_{x=1}^{x_{g}}\psi(x,y\big{|}\mu)\right] ^{2}}{\kappa_{r}(\mu)^{2}} \tag{23}\]  
Additionally, RLNN measures the similarity of run lengths throughout the image, with a lower value indicating more homogeneity among run lengths in the image. This is the normalized version of the RLN formula. Run percentage (RP) is given in Equation (24).  
\[RP=\frac{\kappa_{r}(\mu)}{\kappa_{p}} \tag{24}\]  
Finally, RP measures the coarseness of the texture by taking the ratio of number of runs and number of voxels in the ROI. Values are in the range 1 RLNN \(\leq\) RP \(\leq\) 1 RLNN \(\leq\) RP \(\leq\) 1, with higher values indicating a larger portion of the ROI consists of short runs (which indicates a finer texture).