Provided by: ants_2.2.0-1ubuntu1_amd64 
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. If a mask image is *not* specified
then the entire image region will be used as the mask region. Note that this is
different than the N3 implementation which uses the results of Otsu thresholding to
define a mask. However, this leads to unknown anatomical regions being included and
excluded during the bias correction.
-r, --rescale-intensities 0/(1)
At each iteration, a new intensity mapping is calculated and applied but there is
nothing which constrains the new intensity range to be within certain values. The
result is that the range can "drift" from the original at each iteration. This
option rescales to the [min,max] range of the original image intensities within the
user-specified mask.
-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.Note that the
shrink factor is only applied to the first two or three dimensions which we assume
are spatial.
-c, --convergence [<numberOfIterations=50x50x50x50>,<convergenceThreshold=0.0>]
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>]
[initialMeshResolution,<splineOrder=3>]
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 default setting is to employ a single
mesh element over the entire domain, i.e., -b [1x1x1,3].
-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
[correctedImage,<biasField>]
The output consists of the bias corrected version of the input image. Optionally,
one can also output the estimated bias field.
--version
Get Version Information.
-v, --verbose (0)/1
Verbose output.
-h
Print the help menu (short version).
--help
Print the help menu. <VALUES>: 1