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.
-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.
-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.
-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.
-h
Print the help menu (short version).
--help
Print the help menu. <VALUES>: 1
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