comp_3D_images_corr: Compute voxel-wise correlations between two groups of 3D...

In kangjian2016/SpatCorrImage: Spatial Adaptive Varying Corraltion Analysis for for Multimodal Neuroimaging Data

Description

This function is to compute voxel-wise spatially varying correlation between two groups of 3D images

Usage

comp_3D_images_corr(img_1, img_2, mask = NULL)

Arguments

img_1	a 4D array of multiple 3D images
img_2	a 4D array of multiple 3D images
mask	a 3D array of maske taking logicdal values. The default is NULL.

Value

a 3D array of the correlation maps

Author(s)

Jian Kang <[e-mail]>

Examples

set.seed(1000)
dim_img = c(10,10,10)
n = 50
grids <- lapply(1:3,function(i) seq(-round(dim_img[i]/2)+1,round(dim_img[i]/2),length=dim_img[i]))
img_1 <- array(rnorm(prod(dim_img)*n),dim=c(dim_img,n))
img_2 <- array(rnorm(prod(dim_img)*n),dim=c(dim_img,n))
cor_region <- create_sphere_mask(grids,radius=2)
cor_region_list <- array(cor_region,dim=dim(img_2))
img_2 <- ifelse(cor_region_list,img_1+rnorm(prod(dim_img)*n,sd=0.5),img_2)
mask <- create_sphere_mask(grids,radius=4)
cor_map <- comp_3D_images_corr(img_1,img_2,mask)
plot_3D_image_slices(cor_map,grids,c(-2,2))

kangjian2016/SpatCorrImage documentation built on Oct. 22, 2021, 1:21 a.m.