Provided by: ants_1.9.2+svn680.dfsg-4_amd64 bug


       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.

       -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

              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.


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