embed_kernel-methods: Generic function to position kernel in a position in image...

In bbuchsbaum/neuroim2: Data Structures for Brain Imaging Data

Generic function to position kernel in a position in image space

Description

Generic function to position kernel in a position in image space

Usage

embed_kernel(x, sp, center_voxel, ...)

## S4 method for signature 'Kernel,NeuroSpace,numeric'
embed_kernel(x, sp, center_voxel, weight = 1)

Arguments

x	the kernel object
sp	the space to embed the kernel
center_voxel	the voxel marking the center of the kernel in the embedded space
...	extra args
weight	multiply kernel weights by this value

Value

An object representing the embedded kernel in the specified space.

Examples

# Create a 3D Gaussian kernel with dimensions 3x3x3 and voxel size 1x1x1
kern <- Kernel(kerndim = c(3,3,3), vdim = c(1,1,1), FUN = dnorm, sd = 1)

# Create a NeuroSpace object to embed the kernel in
space <- NeuroSpace(c(10,10,10), c(1,1,1))

# Embed the kernel at the center of the space (position 5,5,5)
embedded_kern <- embed_kernel(kern, space, c(5,5,5))

# The result is a SparseNeuroVol with kernel weights centered at (5,5,5)
# We can also scale the kernel weights by using the weight parameter
embedded_kern_scaled <- embed_kernel(kern, space, c(5,5,5), weight = 2)

# The scaled kernel has weights twice as large as the original
max(values(embedded_kern_scaled)) == 2 * max(values(embedded_kern))

bbuchsbaum/neuroim2 documentation built on Feb. 26, 2025, 3:49 p.m.