Provided by: ants_2.2.0-1ubuntu1_amd64 bug


       antsJointTensorFusion - part of ANTS registration suite



              antsJointTensorFusion  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

       -r, --retain-label-posterior-images (0)/1

              Retain label posterior probability  images.  Requires  atlas  segmentations  to  be
              specified. Default = false

       -f, --retain-atlas-voting-images (0)/1

              Retain atlas voting images. Default = false

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

              Constrain solution to non-negative weights.

       -u, --log-euclidean (0)/1

              Use log Euclidean space for tensor math

       -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

              Search radius for similarity measures. Default = 3x3x3

       -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