Differential bias correction (DBC) was applied to the registered baseline and repeat images using a kernel radius of 5 voxels, to correct for differences in intensity inhomogeneity artifacts between the two images [41].
First, the reflectivity image was smoothed using a kernel of 3 × 3 pixels.
In halftoning process, it needs to use a kernel to carry out the conversion, and the common kernel is like Floyd-Steinberg kernel and is difficult to recover a continuous-tone image through halftone manipulation, conversion, compression, and so forth.
Additionally, we filter this modified image using a Gaussian kernel, which results in a strongly smoothed image free of specular highlights, which is similar to the median filtered image in the segmentation algorithm.
The proportions of perfect (translucent), immature, damaged, abortive, and colored kernels were determined using a grain image analyzer, ES-1000 (Shizuoka Seiki) for "Koshihikari".
The edge image is obtained by filtering the original image using a Sobel kernel.
First convolving the input image using a Gaussian kernel and then applying the Laplacian operator: (4) L x, y = I x, y ∗ G σ x, y ∗ h x, y, where G σ (x, y) represents a Gaussian kernel with σ standard deviations, h x, y) represents a 3 × 3 discrete Laplacian operator [ 32, 33], I x, y) represents the input image, and L x, y) represents the output image.
This is achieved by using a Gaussian kernel centred on the mean image intensity of the original image given by L i ; m o, σ = e x p - i - m o 2 2 σ 2 (4).
Functional images were realigned and unwarped, co-registered with the anatomical image, normalized using parameters obtained from unified segmentation of the anatomical image, and smoothed using a Gaussian kernel of 8 mm FWHM.
The resulting DVR and R1 images were convolved using a Gaussian kernel (FWHM = 1.2 mm).
