Table 1.

The Image Pre-Processor Used in the Study Hypen Its Purposes, the Filter Name, and Its Description

processing Purpose	Preprocessing filters	Description
Image smoothing	Average Smooth	Average filter comes in the classification of windowed filter of linear class which are used to remove noise and smoothing images. The concept works on averaging the neighbours of any element of the image. 2D averaging filter in slice-by-slice. Kernal is a square matrix.
	Edge Preserve_Smooth3D	Anisotropic diffusion smoothing and edge enhancement in 3D.
	Gaussian Smooth	Gaussian filters are a class of linear smoothing filters with the weights chosen according to the shape of a Gaussian function. The Gaussian smoothing filter is a good filter for removing noise drawn from a normal distribution. 2D Gaussian smoothing in slice-by-slice.
	Gaussian_Smooth3D	Gaussian filters are a class of linear smoothing filters with the weights chosen according to the shape of a Gaussian function. The Gaussian smoothing filter is a good filter for removing noise drawn from a normal distribution. Gaussian smoothing in 3D.
	Median Smooth	Median filter is a nonlinear filtering technique also used to remove noise and smoothing images unlike mean filter it preserves useful details in the image 2D median filter in slice-by-slice.
	Wiener Smooth	The Wiener filter is the mean squared error optimal linear filter for blurred images and images contaminated with additive noise. This filter requires the information about the spectra of the noise and the original signal. This method is to delur the image using 2D pixel wise adaptive wiener filter in slice-by-slice.
Image enhancement	AdaptHistEqualization_Enhance3D	Adaptive histogram equalization is a technique used for contrast improvement. It computes several histograms corresponding to distinct image sections and then uses these sections of images to redistribute brightness value of the image. This method is to perform adaptive histogram equalization in 3D.
	Histogram Equalization Enhance	This method is to perform 3D contrast enhancement using histogram equalization.
Image deblur	Blind Deblur	This method is to delur image using blind deconvolution.
	Gaussian Deblur	This method is to deblur 2D image using Lucy-Richardson method and Gaussian PSF in slice-by-slice.
Change enhancement	Laplacian Filter	2D Laplacian filter in slice-by-slice.
	Log Filter	2D Laplacian of Gaussian filter in slice-by-slice.
	X Edge Enhance	2D sobel horizontal edge-emphasizing filter in slice-by-slice.
	Y Edge Enhance	2D sobel vertical edge-emphasizing filter in slice-by-slice.
Resample	Resample Up Down Sample	In the test review window, the original image is also resampled accordingly. This method is to up-sample or down-sample image in 3D.
	Resample Voxel Size	In the test review window, the original image is also resampled accordingly. This method is to resample the pixel size in 3D.
Miscellaneous	Threshold Image Mask	This method is to modify image and mask by applying image intensity threshold and 2D binary mask erosion.
	Threshold Mask	This method is to modify mask only by applying image intensity threshold and 2D binary mask erosion.
	Bit Depth Rescale Range	There are many instances where you may want to scale data from a higher to a lower bit depth. For example, you can prepare data for visual display by scaling it from 16-bit or 32-bit to 8-bit. You can also scale data to a lower bit depth before you export it to applications that do not support data bits greater than 8-bit. By scaling, you can change 32-bit real data from a real number to a whole number and you can scale to reduce the size of your imagery. However, there is a risk of losing information when you scale to reduce file size. This method is to scale the image intensity into the certain bit range.