Table 4.

Time complexity of the CS-LBP-based automatic classification model

Step	Time cost
ROI-based segmentation	$O(w\times h)$
Image resizing	$O(w\times h)$
Divide patches into adrenal gland image	$O(w\times h)$
Feature extraction with CS-LBP from each patch	$O(m\times n\times t)$
Feature concatenation	$O(k)$
Feature selection	$O(k)$
Classification	$O\left(d\right)$
Total	$O(3\times w\times h+m\times n\times t+2k+d)$

$w$ and $h$ are the width and height of the raw CT image, respectively. m and n are the width and height of the patches, respectively. For the developed model, $m$ and $n$ are $16$. $t$ is the number of patches. $k$ is the size of the feature vector, and $d$ is the size of the selected features