Table 1.

Pre-processing algorithms and filters commonly used before image segmentation

Pre-processing technique	Effect on image
Resampling	Changing the number of pixels in the image using interpolation (linear, polynomial, spline, etc.)
Normalization or intensity standardization	Changing the range of pixel intensity values, in order to remove bias, scaling factors and outliers from the image
Quantization of gray levels	Reduction of gray levels used to represent the image
Motion correction	Reduction of motion confounds
Filtering to remove noise and/or improve image characteristics	Laplacian: bringing out area of rapid intensity change and usually used for edge detection Gaussian: smoothing the image and reducing noise Edge filters: resulting in edge enhancement by calculating an approximation of the derivatives in horizontal and vertical directions Laws’ filters: emphasizing image textures of edge, spot, ripple, wave, undulation and oscillation Wavelet filtering or transform methods: decomposing the original image and offering some advantages, such as variation of the spatial resolution (to represent textures at the most appropriate scale), enhancement of the texture appearance and a very wide range of choices for the wavelet function that can be adjusted for specific applications
Inhomogeneity correction	performed on MR images, where the residual effect of the variation of intensity, mainly caused by static magnetic field inhomogeneity and imperfections of the radiofrequency coils, is not eliminated by the previous normalization