Two and three dimensional image registration

The niftyreg function performs linear or nonlinear registration for two and three dimensional images. 4D images may also be registered volumewise to a 3D image, or 3D images slicewise to a 2D image. This function is a common wrapper for niftyreg.linear and niftyreg.nonlinear.

niftyreg(source, target, scope = c("affine", "rigid", "nonlinear"),
  init = NULL, sourceMask = NULL, targetMask = NULL,
  symmetric = TRUE, interpolation = 3L, estimateOnly = FALSE,
  sequentialInit = FALSE, internal = NA, precision = c("double",
  "single"), threads = getOption("RNiftyReg.threads"), ...)

# S3 method for niftyreg as.array(x, ...)


The source image, an object of class "nifti" or "internalImage", or a plain array, or a NIfTI-1 filename. Must have 2, 3 or 4 dimensions.


The target image, an object of class "nifti" or "internalImage", or a plain array, or a NIfTI-1 filename. Must have 2 or 3 dimensions.


A string describing the scope, or number of degrees of freedom (DOF), of the registration. The currently supported values are "affine" (12 DOF), "rigid" (6 DOF) or "nonlinear" (high DOF, with the exact number depending on the image sizes).


Transformation(s) to be used for initialisation, which may be NULL, for no initialisation, or an affine matrix or control point image (nonlinear only). For multiple registration, where the source image has one more dimension than the target, this may also be a list whose components are likewise NULL or a suitable initial transform.


An optional mask image in source space, whose nonzero region will be taken as the region of interest for the registration. Ignored when symmetric is FALSE.


An optional mask image in target space, whose nonzero region will be taken as the region of interest for the registration.


Logical value. Should forward and reverse transformations be estimated simultaneously?


A single integer specifying the type of interpolation to be applied to the final resampled image. May be 0 (nearest neighbour), 1 (trilinear) or 3 (cubic spline). No other values are valid.


Logical value: if TRUE, transformations will be estimated, but images will not be resampled.


If TRUE and source has higher dimensionality than target, transformations which are not explicitly initialised will begin from the result of the previous registration.


If NA, the default, the final resampled image will be returned as a standard R array, but control point maps will be objects of class "internalImage", containing only basic metadata and a C-level pointer to the full image. (See also readNifti.) If TRUE, all image-type objects in the result will be internal images; if FALSE, they will all be R arrays. The default is fine for most purposes, but using TRUE may save memory, while using FALSE can be necessary if there is a chance that external pointers will be invalidated, for example when returning from worker threads.


Working precision for the registration. Using single- precision may be desirable to save memory when coregistering large images.


For OpenMP-capable builds of the package, the maximum number of threads to use.


Further arguments to niftyreg.linear or niftyreg.nonlinear.


A "niftyreg" object.


A list of class "niftyreg" with components:


An array or internal image representing the registered and resampled source image in the space of the target image. This element is NULL if the estimateOnly parameter is TRUE.


A list of (linear or nonlinear) transformations from source to target space.


A list of (linear or nonlinear) transformations from target to source space.


A list of integer vectors, giving the number of iterations completed at each ``level'' of the algorithm. Note that for the first level of the linear algorithm specifically, twice the specified number of iterations is allowed.


An internal representation of the source image for each registration.


An internal representation of the target image.

The as.array method for this class returns the image element.


If substantial parts of the target image are zero-valued, for example because the target image has been brain-extracted, it can be useful to pass it as a target mask as well as the target image, viz. niftyreg(source, target, targetMask=target).


Please see niftyreg.linear or niftyreg.nonlinear for references relating to each type of registration.

See Also

niftyreg.linear and niftyreg.nonlinear, which do most of the work. Also, forward and reverse to extract transformations, and applyTransform to apply them to new images or points.

  • niftyreg
  • as.array.niftyreg
source <- readNifti(system.file("extdata", "epi_t2.nii.gz",
target <- readNifti(system.file("extdata", "flash_t1.nii.gz",

result <- niftyreg(source, target, scope="affine")
# }
# }
Documentation reproduced from package RNiftyReg, version 2.6.7, License: GPL-2

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