Provided by: ants_2.2.0-1ubuntu1_amd64 bug


       antsJointFusion - part of ANTS registration suite



              antsJointFusion  is  an  image  fusion algorithm developed by Hongzhi Wang and Paul
              Yushkevich which won segmentation challenges at MICCAI 2012 and  MICCAI  2013.  The
              original  label  fusion  framework was extended to accommodate intensities by Brian
              Avants. This implementation is based on Paul's  original  ITK-style  implementation
              and  Brian's  ANTsR  implementation.  References  include 1) H. Wang, J. W. Suh, S.
              Das, J. Pluta, C. Craige, P. Yushkevich, Multi-atlas segmentation with joint  label
              fusion  IEEE  Trans.  on Pattern Analysis and Machine Intelligence, 35(3), 611-623,
              2013. and 2) H. Wang and P. A.  Yushkevich,  Multi-atlas  segmentation  with  joint
              label  fusion  and  corrective  learning--an  open  source  implementation,  Front.
              Neuroinform., 2013.

       -d, --image-dimensionality 2/3/4

              This option forces the image to be treated as a specified-dimensional image. If not
              specified, the program tries to infer the dimensionality from the input image.

       -t, --target-image targetImage

              The  target  image  (or multimodal target images) assumed to be aligned to a common
              image domain.

       -g, --atlas-image atlasImage

              The atlas image (or multimodal atlas images) assumed to  be  aligned  to  a  common
              image domain.

       -l, --atlas-segmentation atlasSegmentation

              The  atlas segmentation images. For performing label fusion the number of specified
              segmentations should be identical to the number of atlas image sets.

       -a, --alpha 0.1

              Regularization term added to matrix Mx for calculating the inverse. Default = 0.1

       -b, --beta 2.0

              Exponent for mapping intensity difference to the joint error. Default = 2.0

       -c, --constrain-nonnegative (0)/1

              Constrain solution to non-negative weights.

       -p, --patch-radius 2

              Patch radius for similarity measures. Default = 2x2x2

       -m, --patch-metric (PC)/MSQ

              Metric to be used in determining  the  most  similar  neighborhood  patch.  Options
              include  Pearson's  correlation  (PC) and mean squares (MSQ). Default = PC (Pearson

       -s, --search-radius 3
              3x3x3 searchRadiusMap.nii.gz

              Search radius for similarity measures. Default = 3x3x3. One  can  also  specify  an
              image  where  the  value at the voxel specifies the isotropic search radius at that

       -e, --exclusion-image label[exclusionImage]

              Specify an exclusion region for the given label.

       -x, --mask-image maskImageFilename

              If a mask image is specified, fusion is only performed in the mask region.

       -o, --output labelFusionImage


              The output is the intensity and/or label fusion image. Additional optional  outputs
              include the label posterior probability images and the atlas voting weight images.


              Get version information.

       -v, --verbose (0)/1

              Verbose output.


              Print the help menu (short version).


              Print the help menu.  <VALUES>: 1