N4BiasFieldCorrection(1)
part of ANTS registration suite
DESCRIPTION
COMMAND:
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N4BiasFieldCorrection
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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:
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-d, --image-dimensionality 2/3/4
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- 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.
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-i, --input-image inputImageFilename
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- 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.
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-x, --mask-image maskImageFilename
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- 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.
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-r, --rescale-intensities 0/(1)
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- 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.
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-w, --weight-image weightImageFilename
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- 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.
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-s, --shrink-factor 1/2/3/(4)/...
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- 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.
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-c, --convergence [<numberOfIterations=50x50x50x50>,<convergenceThreshold=0.0>]
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- 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>]
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[initialMeshResolution,<splineOrder=3>]
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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].
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-t, --histogram-sharpening [<FWHM=0.15>,<wienerNoise=0.01>,<numberOfHistogramBins=200>]
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- 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.
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-o, --output correctedImage
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[correctedImage,<biasField>]
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- The output consists of the bias corrected version of the input image.
Optionally, one can also output the estimated bias field.
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--version
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- Get Version Information.
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-v, --verbose (0)/1
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- Verbose output.
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-h
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- Print the help menu (short version).
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--help
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- Print the help menu.
<VALUES>: 1