View source: R/multichannelPCA.R
multichannelPCA | R Documentation |
Converts a set of multichannel images (e.g. deformation fields ) to a matrix to enable various forms of PCA. Returns the components of shape variability and variance explained. May employ different decomposition methods (WIP).
multichannelPCA(
x,
mask,
k = NA,
pcaOption = "PCA",
auxiliaryModality,
center = TRUE,
sigma = NA,
verbose = FALSE
)
x |
list containing multichannel images all of the same size |
mask |
mask to apply to the multichannel images |
k |
rank to use |
pcaOption |
currently only PCA and randPCA, latter being much faster. We also allow fastICA. |
auxiliaryModality |
if you pass this matrix, then will do CCA. This will only work with one option. |
center |
subtract the mean column vector. |
sigma |
parameter for kernel PCA. |
verbose |
produces more explanatory output. |
list of the pca output and conversion to multichannel images
Avants BB
img1 <- antsImageRead(getANTsRData("r16")) %>%
resampleImage(c(4, 4))
img2 <- antsImageRead(getANTsRData("r64")) %>%
resampleImage(c(4, 4))
img3 <- antsImageRead(getANTsRData("r27")) %>%
resampleImage(c(4, 4))
img4 <- antsImageRead(getANTsRData("r30")) %>%
resampleImage(c(4, 4))
reg1 <- antsRegistration(img1, img2, "SyN")
reg2 <- antsRegistration(img1, img3, "SyN")
reg3 <- antsRegistration(img1, img4, "SyN")
w1 <- antsImageRead(reg1$fwdtransforms[1])
w2 <- antsImageRead(reg2$fwdtransforms[1])
w3 <- antsImageRead(reg3$fwdtransforms[1])
mask <- getMask(img1)
x <- list(w1, w2, w3)
dpca <- multichannelPCA(x, mask)
warpTx <- antsrTransformFromDisplacementField(dpca$pcaWarps[[1]])
warped <- applyAntsrTransform(warpTx, data = img1, reference = img1)
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