ANTS(1) User Commands ANTS(1)NAME
ANTS - part of ANTS registration suite
DESCRIPTION
Example usage:
./ANTS ImageDimension -m MI[fixedimage.nii.gz,movingimage.nii.gz,1,32] -o Outputfname.nii.gz -i 30x20x0 -r Gauss[3,1] -t Elast[3]
Compulsory arguments:
ImageDimension: 2 or 3 (for 2 or 3 Dimensional registration)
-m: Type of similarity model used for registration.
For intramodal image registration, use:
CC = cross-correlation MI = mutual information PR = probability mapping MSQ = mean square difference
For intermodal image registration, use:
MI = mutual information PR = probability mapping
-o Outputfname.nii.gz: the name of the resulting image.
-i Max-iterations in format: JxKxL, where:
J = max iterations at coarsest resolution (here, reduce by power of 2^2) K = middle resolution iterations (here,reduce by power of
2) L = fine resolution iterations (here, full resolution). This level takes much more time per iteration!
Adding an extra value before JxKxL (i.e. resulting in IxJxKxL) would add another iteration level.
-r Regularization
-t Type of transformation model used for registration
For elastic image registration, use:
Elast = elastic transformation model (less deformation possible)
For diffeomorphic image registration, use:
Syn[GradStep,TimePoints,IntegrationStep] --geodesic 2 = SyN with time with arbitrary number of time points in time discretization
SyN[GradStep,2,IntegrationStep] = SyN with time optimized specifically for 2 time points in the time discretization SyN[GradStep] =
Greedy SyN, typicall GradStep=0.25 Exp[GradStep,TimePoints] = Exponential GreedyExp = Diffeomorphic Demons style exponential mapping
Please use the `ANTS -h ` call or refer to the ANTS.pdf manual or antsIntroduction.sh script for additional information and typical
values for transformation models
ANTS 1.9 May 2012 ANTS(1)
Check Out this Related Man Page
N4BIASFIELDCORRECTION(1) User Commands N4BIASFIELDCORRECTION(1)NAME
N4BiasFieldCorrection - part of ANTS registration suite
DESCRIPTION
COMMAND:
./N4BiasFieldCorrection
N4 is a variant of the popular N3 (nonparameteric nonuniform normalization) retrospective bias correction algorithm. Based on the
assumption that the corruption of the low frequency bias field can be modeled as a convolution of the intensity histogram by a
Gaussian, the basic algorithmic protocol is to iterate between deconvolving the intensity histogram by a Gaussian, remapping the
intensities, and then spatially smoothing this result by a B-spline modeling of the bias field itself. The modifications from and
improvements obtained over the original N3 algorithm are described in the following paper: N. Tustison et al., N4ITK: Improved N3
Bias Correction, IEEE Transactions on Medical Imaging, 29(6):1310-1320, June 2010.
OPTIONS:
-d, --image-dimensionality 2/3/4
This option forces the image to be treated as a specified-dimensional image. If not specified, N4 tries to infer the dimensionality
from the input image.
-i, --input-image inputImageFilename
A scalar image is expected as input for bias correction. Since N4 log transforms the intensities, negative values or values close to
zero should be processed prior to correction.
-x, --mask-image maskImageFilename
If a mask image is specified, the final bias correction is only performed in the mask region. If a weight image is not specified,
only intensity values inside the masked region are used during the execution of the algorithm. If a weight image is specified, only
the non-zero weights are used in the execution of the algorithm although the mask region defines where bias correction is performed
in the final output. Otherwise bias correction occurs over the entire image domain. See also the option description for the weight
image.
-w, --weight-image weightImageFilename
The weight image allows the user to perform a relative weighting of specific voxels during the B-spline fitting. For example, some
studies have shown that N3 performed on white matter segmentations improves performance. If one has a spatial probability map of the
white matter, one can use this map to weight the b-spline fitting towards those voxels which are more probabilistically classified
as white matter. See also the option description for the mask image.
-s, --shrink-factor 1/2/3/4/...
Running N4 on large images can be time consuming. To lessen computation time, the input image can be resampled. The shrink factor,
specified as a single integer, describes this resampling. Shrink factors <= 4 are commonly used.
-c, --convergence [<numberOfIterations=50>,<convergenceThreshold=0.001>]
Convergence is determined by calculating the coefficient of variation between subsequent iterations. When this value is less than
the specified threshold from the previous iteration or the maximum number of iterations is exceeded the program terminates. Multiple
resolutions can be specified by using 'x' between the number of iterations at each resolution, e.g. 100x50x50.
-b, --bspline-fitting [splineDistance,<splineOrder=3>,<sigmoidAlpha=0.0>,<sigmoidBeta=0.5>]
[initialMeshResolution,<splineOrder=3>,<sigmoidAlpha=0.0>,<sigmoidBeta=0.5>]
These options describe the b-spline fitting parameters. The initial b-spline mesh at the coarsest resolution is specified either as
the number of elements in each dimension, e.g. 2x2x3 for 3-D images, or it can be specified as a single scalar parameter which
describes the isotropic sizing of the mesh elements. The latter option is typically preferred. For each subsequent level, the spline
distance decreases in half, or equivalently, the number of mesh elements doubles Cubic splines (order = 3) are typically used. The
final two parameters are experimental and really do not need to be used for good performance.
-t, --histogram-sharpening [<FWHM=0.15>,<wienerNoise=0.01>,<numberOfHistogramBins=200>]
These options describe the histogram sharpening parameters, i.e. the deconvolution step parameters described in the original N3
algorithm. The default values have been shown to work fairly well.
-o, --output [correctedImage,<biasField>]
The output consists of the bias corrected version of the input image. Optionally, one can also output the estimated bias field.
-h
Print the help menu (short version). <VALUES>: 0
--help
Print the help menu. <VALUES>: 1, 0
N4BiasFieldCorrection 1.9 May 2012 N4BIASFIELDCORRECTION(1)