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.

       -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=50>,<convergenceThreshold=0.001>]

              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>,<sigmoidAlpha=0.0>,<sigmoidBeta=0.5>]
              [initialMeshResolution,<splineOrder=3>,<sigmoidAlpha=0.0>,<sigmoidBeta=0.5>]

              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 final two parameters are experimental and really do
              not need to be used for good performance.

       -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,<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).  <VALUES>: 0

       --help

              Print the help menu.  <VALUES>: 1, 0