bionic (1) N4BiasFieldCorrection.1.gz
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