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       N4BiasFieldCorrection - part of ANTS registration suite



              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.

       -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>]

              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


              The  output consists of the bias corrected version of the input image.  Optionally,
              one can also output the estimated bias field.


              Get Version Information.

       -v, --verbose (0)/1

              Verbose output.


              Print the help menu (short version).


              Print the help menu.  <VALUES>: 1