Description Usage Arguments Details Value Author(s) References See Also
The niftyreg.linear
function performs linear 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. Rigidbody (6 degrees of
freedom) and affine (12 degrees of freedom) registration can currently be
performed.
1 2 3 4 5  niftyreg.linear(source, target, scope = c("affine", "rigid"), init = NULL,
sourceMask = NULL, targetMask = NULL, symmetric = TRUE, nLevels = 3L,
maxIterations = 5L, useBlockPercentage = 50L, interpolation = 3L,
verbose = FALSE, estimateOnly = FALSE, sequentialInit = FALSE,
internal = NA, precision = c("double", "single"))

source 
The source image, an object of class 
target 
The target image, an object of class 
scope 
A string describing the scope, or number of degrees of freedom
(DOF), of the registration. The currently supported values are

init 
Transformation(s) to be used for initialisation, which may be

sourceMask 
An optional mask image in source space, whose nonzero
region will be taken as the region of interest for the registration.
Ignored when 
targetMask 
An optional mask image in target space, whose nonzero region will be taken as the region of interest for the registration. 
symmetric 
Logical value. Should forward and reverse transformations be estimated simultaneously? 
nLevels 
A single integer specifying the number of levels of the algorithm that should be applied. If zero, no optimisation will be performed, and the final affine matrix will be the same as its initialisation value. 
maxIterations 
A single integer specifying the maximum number of iterations to be used within each level. Fewer iterations may be used if a convergence test deems the process to have completed. 
useBlockPercentage 
A single integer giving the percentage of blocks to use for calculating correspondence at each step of the algorithm. The blocks with the highest intensity variance will be chosen. 
interpolation 
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. 
verbose 
A single logical value: if 
estimateOnly 
Logical value: if 
sequentialInit 
If 
internal 
If 
precision 
Working precision for the registration. Using single precision may be desirable to save memory when coregistering large images. 
This function performs the dual operations of finding a transformation to optimise image alignment, and resampling the source image into the space of the target image.
The algorithm is based on a blockmatching approach and Least Trimmed Squares (LTS) fitting. Firstly, the block matching provides a set of corresponding points between a target and a source image. Secondly, using this set of corresponding points, the best rigid or affine transformation is evaluated. This twostep loop is repeated until convergence to the best transformation is achieved.
In the NiftyReg implementation, normalised crosscorrelation between the target and source blocks is used to evaluate correspondence. The block width is constant and has been set to 4 voxels. A coarsetofine approach is used, where the registration is first performed on downsampled images (using a Gaussian filter to resample images), and finally performed on full resolution images.
The source image may have 2, 3 or 4 dimensions, and the target 2 or 3. The dimensionality of the target image determines whether 2D or 3D registration is applied, and source images with one more dimension than the target (i.e. 4D to 3D, or 3D to 2D) will be registered volumewise or slicewise, as appropriate. In the latter case the last dimension of the resulting image is taken from the source image, while all other dimensions come from the target. One affine matrix is returned for each registration performed.
See niftyreg
.
Jon Clayden <[email protected]>
The algorithm used by this function is described in the following publication.
M. Modat, D.M. Cash, P. Daga, G.P. Winston, J.S. Duncan & S. Ourselin (2014). Global image registration using a symmetric blockmatching approach. Journal of Medical Imaging 1(2):024003.
niftyreg
, which can be used as an interface to this
function, and niftyreg.nonlinear
for nonlinear registration.
Also, forward
and reverse
to extract
transformations, and applyTransform
to apply them to new
images or points.
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