randomlyTransformImageData: Randomly transform image data (optional: with corresponding...

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

Randomly transform image data (optional: with corresponding segmentations).

Description

Apply rigid, affine and/or deformable maps to an input set of training images. The reference image domain defines the space in which this happens.

Usage

randomlyTransformImageData(
  referenceImage,
  inputImageList,
  segmentationImageList = NULL,
  numberOfSimulations = 10,
  transformType = "affine",
  sdAffine = 0.02,
  deformationTransformType = c("bspline", "exponential"),
  numberOfRandomPoints = 1000,
  sdNoise = 10,
  numberOfFittingLevels = 4,
  meshSize = 1,
  sdSmoothing = 4,
  inputImageInterpolator = "linear",
  segmentationImageInterpolator = "nearestNeighbor"
)

Arguments

referenceImage	defines the spatial domain for all output images. If the input images do not match the spatial domain of the reference image, we internally resample the target to the reference image. This could have unexpected consequences. Resampling to the reference domain is performed by testing using antsImagePhysicalSpaceConsistency then calling resampleImageToTarget upon failure.
inputImageList	list of lists of input images to warp. The internal list sets contains one or more images (per subject) which are assumed to be mutually aligned. The outer list contains multiple subject lists which are randomly sampled to produce output image list.
segmentationImageList	list of segmentation images corresponding to the input image list (optional).
numberOfSimulations	number of output images. Default = 10.
transformType	one of the following options c( "translation", "rigid", "scaleShear", "affine"," deformation" , "affineAndDeformation" ). Default = \"affine\".
sdAffine	parameter dictating deviation amount from identity for random linear transformations. Default = 0.02.
deformationTransformType	one of the following options c( "bspline", "exponential" ) if deformation is specified in the transformType. Default = \"bspline\".
numberOfRandomPoints	number of displacement points for the deformation field. Default = 1000.
sdNoise	standard deviation of the displacement field noise (in mm). Default = 10.0.
numberOfFittingLevels	(bspline deformation only) number of fitting levels. Default = 4.
meshSize	(bspline deformation only) scalar or n-D vector determining fitting resolution. Default = 1.
sdSmoothing	(exponential deformation only) standard deviation of the Gaussian smoothing in mm. Default = 4.0.
inputImageInterpolator	one of the following options c( "linear", "gaussian", "bspline" ). Default = \"linear\".
segmentationImageInterpolator	one of the following options c( "nearestNeighbor", "genericLabel" ). Default = \"nearestNeighbor\".

Value

list (if no directory set) or boolean for success, failure

Author(s)

Avants BB, Tustison NJ

Examples

library( ANTsR )
image1 <- antsImageRead( getANTsRData( "r16" ) )
image2 <- antsImageRead( getANTsRData( "r64" ) )
segmentation1 <- thresholdImage( image1, "Otsu", 3 )
segmentation2 <- thresholdImage( image2, "Otsu", 3 )
data <- randomlyTransformImageData( image1,
  list( list( image1 ), list( image2 ) ),
  list( segmentation1, segmentation2 ) )
rm(segmentation1); gc()
rm(segmentation2); gc()
rm(image1); gc()
rm(image2); gc()

ANTsX/ANTsRNet documentation built on April 23, 2024, 1:24 p.m.