Table 1.

Spatial features used in classifiers.

Feature	Equation
Mean	$\mu =\frac{1}{n}{\displaystyle \sum _{i=1}^n}{x}_i$
Median	$\tilde{x}=\frac{x\lceil nx÷2\rceil +x\lceil nx÷2+1\rceil }{2}$
Variance	${\sigma }^2=\frac{1}{n}{\displaystyle \sum _{i=1}^n}{\left(x_i-\mu \right)}^2$
Standard deviation	$\sigma =\sqrt{\frac{1}{n}{\displaystyle \sum _{i=1}^n}{\left(x_i-\mu \right)}^2}$
Trimmed mean	${\mu }_{.25}=\frac{1}{n}{\displaystyle \sum _{i=1}^n}{x}_i\left(\mathrm{excludes}25\%\mathrm{high}\&\mathrm{low}\right)$
Coefficient of variance	$\widehat{C_v}=\frac{\sigma }{\tilde{x}}$
Kurtosis	$k=\left\{\frac{n\left(n+1\right)}{\left(n-1\right)\left(n-3\right)\frac{{\sum }_{i=1}^n{\left(x_i-\tilde{x}\right)}^4}{{\sigma }^4}}\right\}-\frac{3{\left(n-1\right)}^2}{\left(n-2\right)\left(n-3\right)}$
Interquartile	$IQR=\frac{3}{4}\left(n+1\right)th-\frac{1}{2}\left(n+1\right)th$
Percentiles (1,10,25,50,75,99)	$i=\frac{n{p}_i}{100}+0.5$
Squared deviation	$SD={\displaystyle \sum _{i=1}^n}{\left(x_i-\tilde{x}\right)}^2$
Average absolute dev.	$AAD=\frac{\sum {i=1}^n|x_i-\tilde{x}|}{n}$