Shape analysis of multichannel images based on PCA

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
)

Arguments

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.

Value

list of the pca output and conversion to multichannel images

Author

Avants BB

Examples

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)