randomImageTransformParametersAugmentation: Generate transform parameters and transformed images

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

Generate transform parameters and transformed images

Description

The function will apply rigid, affine or deformable maps to an input set of training images. The reference image domain defines the space in which this happens. The outcome here is the transform parameters themselves. This is intended for use with low-dimensional transformations.

Usage

randomImageTransformParametersAugmentation(
  imageDomain,
  predictorImageList,
  n = 8,
  typeOfTransform = "Affine",
  interpolator = "linear",
  spatialSmoothing = 3,
  numberOfCompositions = 4,
  deformationBasis,
  txParamMeans,
  txParamSDs,
  center = FALSE
)

Arguments

imageDomain	defines the spatial domain for all images. NOTE: if the input images do not match the spatial domain of the domain image, we internally resample the target to the domain. This may have unexpected consequences if you are not aware of this. This operation will test antsImagePhysicalSpaceConsistency then call resampleImageToTarget upon failure.
predictorImageList	list of lists of image predictors
n	number of simulations to run
typeOfTransform	one of the following options c("Translation","Rigid","ScaleShear","Affine", "DeformationBasis")
interpolator	nearestNeighbor or linear (string) for predictor images
spatialSmoothing	spatial smoothing for simulated deformation
numberOfCompositions	integer greater than or equal to one
deformationBasis	list containing deformationBasis set or a matrix if the basis is low-dimensional i.e. affine
txParamMeans	list containing deformationBasis set means
txParamSDs	list containing deformationBasis standard deviations
center	center the parameters before passing as ground truth output

Value

list of transformed images and transform parameters

Author(s)

Avants BB

See Also

randomImageTransformParametersBatchGenerator

Examples

library( ANTsR )
i1 = antsImageRead( getANTsRData( "r16" ) )
i2 = antsImageRead( getANTsRData( "r64" ) )
s1 = thresholdImage( i1, "Otsu", 3 )
s2 = thresholdImage( i2, "Otsu", 3 )
rand = randomImageTransformParametersAugmentation( i1,
  list( i1, i2 ), txParamMeans=c(1,0,0,1), txParamSDs=diag(4)*0.01 )

ANTsX/ANTsRNet documentation built on April 18, 2024, 8 a.m.