Provided by: mia-tools_2.2.7-3_amd64 bug

NAME

       mia-3dprealign-nonrigid - Registration of a series of 3D images.

SYNOPSIS

       mia-3dprealign-nonrigid -i <in-file> -o <out-file> [options]

DESCRIPTION

       mia-3dprealign-nonrigid This program runs the non-rigid registration of an image series by
       first registering an already aligned subset of the images to one reference,  and  then  by
       registering  the remaining images by using synthetic references. The is a 3D version of G.
       Wollny, M-J Ledesma-Cabryo,  P.Kellman,  and  A.Santos,  "Exploiting  Quasiperiodicity  in
       Motion Correction of Free-Breathing," IEEE Transactions on Medical Imaging, 29(8), 2010.

OPTIONS

   File-IO
              -i --in-file=(input, required); io
                     input  images  following the naming pattern nameXXXX.ext  For supported file
                     types see PLUGINS:3dimage/io

              -o --out-file=(output, required); io
                     file name base for registered files given as C-format string  For  supported
                     file types see PLUGINS:3dimage/io

                 --save-references
                     Save synthetic references to files refXXXX.v

   Preconditions & Preprocessing
              -k --skip=0
                     Skip images at the begin of the seriesSkip images at the begin of the series

                 --preskip=20
                     Skip  images  at  the  beginning+skip  of the series when searching for high
                     contrats imageSkip images at the beginning+skip of the series when searching
                     for high contrats image

                 --postskip=2
                     Skip  images  at  the  end  of  the  series when searching for high contrats
                     imageSkip images at the end of the series when searching for  high  contrats
                     image

                 --max-candidates=20
                     maximum  number  of  candidates  for global reference imagemaximum number of
                     candidates for global reference image

              -S --cost-series=image:cost=[ngf:eval=ds]
                     Const function to use for the analysis of the seriesConst  function  to  use
                     for    the   analysis   of   the   series    For   supported   plugins   see
                     PLUGINS:3dimage/fullcost

                 --ref-idx=
                     save reference index number to this file

              -R --global-reference=-1
                     save reference index number to this filesave reference index number to  this
                     file

              -D --max-subset-delta=0
                     Maximum  delta  between  two  elements of the prealigned subsetMaximum delta
                     between two elements of the prealigned subset

   Registration
              -O --optimizer=gsl:opt=gd,step=0.01
                     Optimizer  used  for  minimizationOptimizer  used  for   minimization    For
                     supported plugins see PLUGINS:minimizer/singlecost

              -l --mr-levels=3
                     multi-resolution levelsmulti-resolution levels

              -f --transForm=spline
                     transformation   typetransformation   type    For   supported   plugins  see
                     PLUGINS:3dimage/transform

              -1 --cost-subset=image:cost=[ngf:eval=ds]
                     Cost function for registration during the subset  registrationCost  function
                     for  registration  during the subset registration  For supported plugins see
                     PLUGINS:3dimage/fullcost

              -2 --cost-final=image:cost=ssd
                     Cost function for registration during the  final  registrationCost  function
                     for  registration  during  the final registration  For supported plugins see
                     PLUGINS:3dimage/fullcost

   Help & Info
              -V --verbose=warning
                     verbosity of output, print messages of given level  and  higher  priorities.
                     Supported priorities starting at lowest level are:
                        info ‐ Low level messages
                        trace ‐ Function call trace
                        fail ‐ Report test failures
                        warning ‐ Warnings
                        error ‐ Report errors
                        debug ‐ Debug output
                        message ‐ Normal messages
                        fatal ‐ Report only fatal errors

                 --copyright
                     print copyright information

              -h --help
                     print this help

              -? --usage
                     print a short help

                 --version
                     print the version number and exit

   Processing
                 --threads=-1
                     Maxiumum number of threads to use for processing,This number should be lower
                     or equal to the number of logical  processor  cores  in  the  machine.  (-1:
                     automatic  estimation).Maxiumum number of threads to use for processing,This
                     number should be lower or equal to the number of logical processor cores  in
                     the machine. (-1: automatic estimation).

PLUGINS: 1d/spacialkernel

       cdiff     Central difference filter kernel, mirror boundary conditions are used.

                     (no parameters)

       gauss     spacial Gauss filter kernel, supported parameters are:

                     w = 1; uint in [0, inf)
                       half filter width.

PLUGINS: 1d/splinebc

       mirror    Spline interpolation boundary conditions that mirror on the boundary

                     (no parameters)

       repeat    Spline interpolation boundary conditions that repeats the value at the boundary

                     (no parameters)

       zero      Spline interpolation boundary conditions that assumes zero for values outside

                     (no parameters)

PLUGINS: 1d/splinekernel

       bspline   B-spline kernel creation , supported parameters are:

                     d = 3; int in [0, 5]
                       Spline degree.

       omoms     OMoms-spline kernel creation, supported parameters are:

                     d = 3; int in [3, 3]
                       Spline degree.

PLUGINS: 3dimage/combiner

       absdiff   Image combiner 'absdiff'

                     (no parameters)

       add       Image combiner 'add'

                     (no parameters)

       div       Image combiner 'div'

                     (no parameters)

       mul       Image combiner 'mul'

                     (no parameters)

       sub       Image combiner 'sub'

                     (no parameters)

PLUGINS: 3dimage/cost

       lncc      local  normalized  cross correlation with masking support., supported parameters
                 are:

                     w = 5; uint in [1, 256]
                       half  width  of  the  window  used  for  evaluating  the  localized  cross
                       correlation.

       mi        Spline parzen based mutual information., supported parameters are:

                     cut = 0; float in [0, 40]
                       Percentage  of  pixels  to  cut  at  high  and  low  intensities to remove
                       outliers.

                     mbins = 64; uint in [1, 256]
                       Number of histogram bins used for the moving image.

                     mkernel = [bspline:d=3]; factory
                       Spline kernel for moving image parzen hinstogram.  For supported  plug-ins
                       see PLUGINS:1d/splinekernel

                     rbins = 64; uint in [1, 256]
                       Number of histogram bins used for the reference image.

                     rkernel = [bspline:d=0]; factory
                       Spline  kernel for reference image parzen hinstogram.  For supported plug-
                       ins see PLUGINS:1d/splinekernel

       ncc       normalized cross correlation.

                     (no parameters)

       ngf       This function evaluates  the  image  similarity  based  on  normalized  gradient
                 fields. Given normalized gradient fields $ _S$ of the src image and $ _R$ of the
                 ref image various evaluators are implemented., supported parameters are:

                     eval = ds; dict
                       plugin subtype (sq, ds,dot,cross).  Supported values are:
                           ds ‐ square of scaled difference
                           dot ‐ scalar product kernel
                           cross ‐ cross product kernel

       ssd       3D image cost: sum of squared differences, supported parameters are:

                     autothresh = 0; float in [0, 1000]
                       Use automatic masking of the moving image by only takeing intensity values
                       into accound that are larger than the given threshold.

                     norm = 0; bool
                       Set whether the metric should be normalized by the number of image pixels.

       ssd-automask
                 3D  image  cost:  sum  of  squared  differences, with automasking based on given
                 thresholds, supported parameters are:

                     rthresh = 0; double
                       Threshold intensity value for reference image.

                     sthresh = 0; double
                       Threshold intensity value for source image.

PLUGINS: 3dimage/filter

       bandpass  intensity bandpass filter, supported parameters are:

                     max = 3.40282e+38; float
                       maximum of the band.

                     min = 0; float
                       minimum of the band.

       binarize  image binarize filter, supported parameters are:

                     max = 3.40282e+38; float
                       maximum of accepted range.

                     min = 0; float
                       minimum of accepted range.

       close     morphological close, supported parameters are:

                     hint = black; string
                       a hint at the main image content (black|white).

                     shape = [sphere:r=2]; factory
                       structuring element.  For supported plug-ins see PLUGINS:3dimage/shape

       combiner  Combine two images with the given combiner operator.  if  'reverse'  is  set  to
                 false,  the  first operator is the image passed through the filter pipeline, and
                 the second image is loaded from the file given with the  'image'  parameter  the
                 moment the filter is run., supported parameters are:

                     image =(input, required, string)
                       second image that is needed in the combiner.

                     op =(required, factory)
                       Image  combiner  to  be applied to the images.  For supported plug-ins see
                       PLUGINS:3dimage/combiner

                     reverse = 0; bool
                       reverse the order in which the images passed to the combiner.

       convert   image pixel format conversion filter, supported parameters are:

                     a = 1; float
                       linear conversion parameter a.

                     b = 0; float
                       linear conversion parameter b.

                     map = opt; dict
                       conversion mapping.  Supported values are:
                           opt ‐ apply a linear transformation that maps the real input range  to
                           the full output range
                           range  ‐  apply  linear  transformation  that maps the input data type
                           range to the output data type range
                           copy ‐ copy data when converting
                           linear ‐ apply linear transformation x -> a*x+b
                           optstat ‐ apply a linear transform that maps based on input  mean  and
                           variation to the full output range

                     repn = ubyte; dict
                       output pixel type.  Supported values are:
                           none ‐ no pixel type defined
                           float ‐ floating point 32 bit
                           sbyte ‐ signed 8 bit
                           ulong ‐ unsigned 64 bit
                           double ‐ floating point 64 bit
                           sint ‐ signed 32 bit
                           ushort ‐ unsigned 16 bit
                           sshort ‐ signed 16 bit
                           uint ‐ unsigned 32 bit
                           slong ‐ signed 64 bit
                           bit ‐ binary data
                           ubyte ‐ unsigned 8 bit

       crop      Crop  a  region  of an image, the region is always clamped to the original image
                 size in the sense that the given range is kept., supported parameters are:

                     end = [[4294967295,4294967295,4294967295]]; streamable
                       end of cropping range, maximum = (-1,-1,-1).

                     start = [[0,0,0]]; streamable
                       begin of cropping range.

       dilate    3d image stack dilate filter, supported parameters are:

                     hint = black; string
                       a hint at the main image content (black|white).

                     shape = [sphere:r=2]; factory
                       structuring element.  For supported plug-ins see PLUGINS:3dimage/shape

       distance  Evaluate the 3D distance transform of an image. If the image is a  binary  mask,
                 then result of the distance transform in each point corresponds to the Euclidian
                 distance to the mask. If the input image is of a scalar pixel  value,  then  the
                 this  scalar  is  interpreted  as heighfield and the per pixel value adds to the
                 distance.

                     (no parameters)

       downscale Downscale the input image by using a given block size to  define  the  downscale
                 factor.  Prior  to  scaling  the  image  is  filtered  by  a smoothing filter to
                 eliminate  high  frequency  data  and  avoid  aliasing   artifacts.,   supported
                 parameters are:

                     b = [[1,1,1]]; 3dbounds
                       blocksize.

                     bx = 1; uint in [1, inf)
                       blocksize in x direction.

                     by = 1; uint in [1, inf)
                       blocksize in y direction.

                     bz = 1; uint in [1, inf)
                       blocksize in z direction.

                     kernel = gauss; string
                       smoothing filter kernel to be applied, the size of the filter is estimated
                       based on the blocksize..

       erode     3d image stack erode filter, supported parameters are:

                     hint = black; string
                       a hint at the main image content (black|white).

                     shape = [sphere:r=2]; factory
                       structuring element.  For supported plug-ins see PLUGINS:3dimage/shape

       gauss     isotropic 3D gauss filter, supported parameters are:

                     w = 1; int in [0, inf)
                       filter width parameter.

       gradnorm  3D image to gradient norm filter

                     (no parameters)

       growmask  Use an input binary mask and a reference gray scale image to do  region  growing
                 by  adding the neighborhood pixels of an already added pixel if the have a lower
                 intensity that is above the given threshold., supported parameters are:

                     min = 1; float
                       lower threshold for mask growing.

                     ref =(input, required, string)
                       reference image for mask region growing.

                     shape = 6n; factory
                       neighborhood mask.  For supported plug-ins see PLUGINS:3dimage/shape

       invert    intensity invert filter

                     (no parameters)

       isovoxel  This filter scales an image to make the voxel size isometric  and  its  size  to
                 correspond to the given value, supported parameters are:

                     interp = [bspline:d=3]; factory
                       interpolation   kernel   to   be   used  .   For  supported  plug-ins  see
                       PLUGINS:1d/splinekernel

                     size = 1; float in (0, inf)
                       isometric target voxel size.

       kmeans    3D image k-means filter. In the output image  the  pixel  value  represents  the
                 class  membership  and  the class centers are stored as attribute in the image.,
                 supported parameters are:

                     c = 3; int in [2, inf)
                       number of classes.

       label     A filter to label the   connected  components  of  a  binary  image.,  supported
                 parameters are:

                     n = 6n; factory
                       neighborhood mask.  For supported plug-ins see PLUGINS:3dimage/shape

       labelmap  Image  filter to remap label id's. Only applicable to images with integer valued
                 intensities/labels., supported parameters are:

                     map =(input, required, string)
                       Label mapping file.

       labelscale
                 A filter that only creates output voxels that are already created in  the  input
                 image.  Scaling  is  done  by  using a voting algorithms that selects the target
                 pixel value based on  the  highest  pixel  count  of  a  certain  label  in  the
                 corresponding  source  region.  If the region comprises two labels with the same
                 count, the one with the lower number wins., supported parameters are:

                     out-size =(required, 3dbounds)
                       target size given as two coma separated values.

       load      Load the input image from a file and use it to replace the current image in  the
                 pipeline., supported parameters are:

                     file =(input, required, string)
                       name of the input file to load from..

       lvdownscale
                 This  is  a  label voting downscale filter. It adownscales a 3D image by blocks.
                 For each block the (non-zero) label that appears most  times  in  the  block  is
                 issued as output pixel in the target image. If two labels appear the same number
                 of times, the one with the lower absolute value wins., supported parameters are:

                     b = [[1,1,1]]; 3dbounds
                       blocksize for the downscaling. Each block will be represented by one pixel
                       in the target image..

       mask      Mask  an  image,  one image is taken from the parameters list and the other from
                 the normal filter input. Both images must be of the same dimensions and one must
                 be  binary.  The  attributes of the image coming through the filter pipeline are
                 preserved. The output pixel type corresponds to the  input  image  that  is  not
                 binary., supported parameters are:

                     input =(input, required, string)
                       second input image file name.

       mean      3D image mean filter, supported parameters are:

                     w = 1; int in [1, inf)
                       half filter width.

       median    median 3d filter, supported parameters are:

                     w = 1; int in [1, inf)
                       filter width parameter.

       mlv       Mean of Least Variance 3D image filter, supported parameters are:

                     w = 1; int in [1, inf)
                       filter width parameter.

       msnormalizer
                 3D image mean-sigma normalizing filter, supported parameters are:

                     w = 1; int in [1, inf)
                       half filter width.

       open      morphological open, supported parameters are:

                     hint = black; string
                       a hint at the main image content (black|white).

                     shape = [sphere:r=2]; factory
                       structuring element.  For supported plug-ins see PLUGINS:3dimage/shape

       reorient  3D image reorientation filter, supported parameters are:

                     map = xyz; dict
                       oriantation mapping to be applied.  Supported values are:
                           p-zxy ‐ permutate x->y->z->x
                           r-x180 ‐ rotate around x-axis clockwise 180 degree
                           xyz ‐ keep orientation
                           p-yzx ‐ permutate x->z->y->x
                           r-z180 ‐ rotate around z-axis clockwise 180 degree
                           r-y270 ‐ rotate around y-axis clockwise 270 degree
                           f-xz ‐ flip x-z
                           f-yz ‐ flip y-z
                           r-x90 ‐ rotate around x-axis clockwise 90 degree
                           r-y90 ‐ rotate around y-axis clockwise 90 degree
                           r-x270 ‐ rotate around x-axis clockwise 270 degree
                           r-z270 ‐ rotate around z-axis clockwise 270 degree
                           r-z90 ‐ rotate around z-axis clockwise 90 degree
                           f-xy ‐ flip x-y
                           r-y180 ‐ rotate around y-axis clockwise 180 degree

       resize    Resize  an  image.  The  original  data is centered within the new sized image.,
                 supported parameters are:

                     size = [[0,0,0]]; streamable
                       new size of the image a  size  0  indicates  to  keep  the  size  for  the
                       corresponding dimension..

       sandp     salt and pepper  3d filter, supported parameters are:

                     thresh = 100; float in [0, inf)
                       thresh value.

                     w = 1; int in [1, inf)
                       filter width parameter.

       scale     3D image filter that scales to a given target size , supported parameters are:

                     interp = [bspline:d=3]; factory
                       interpolation   kernel   to   be   used  .   For  supported  plug-ins  see
                       PLUGINS:1d/splinekernel

                     s = [[0,0,0]]; 3dbounds
                       target size to set all components at once  (component  0:use  input  image
                       size).

                     sx = 0; uint in [0, inf)
                       target size in x direction (0:use input image size).

                     sy = 0; uint in [0, inf)
                       target size in y direction (0:use input image size).

                     sz = 0; uint in [0, inf)
                       target size in y direction (0:use input image size).

       selectbig A  filter  that creats a binary mask representing the intensity with the highest
                 pixel count.The pixel value 0 will be ignored, and if two intensities  have  the
                 same  pixel  count,  then  the result is undefined. The input pixel must have an
                 integral pixel type.

                     (no parameters)

       sepconv   3D image intensity separaple convolution filter, supported parameters are:

                     kx = [gauss:w=1]; factory
                       filter   kernel   in   x-direction.    For    supported    plug-ins    see
                       PLUGINS:1d/spacialkernel

                     ky = [gauss:w=1]; factory
                       filter    kernel    in    y-direction.    For   supported   plug-ins   see
                       PLUGINS:1d/spacialkernel

                     kz = [gauss:w=1]; factory
                       filter   kernel   in   z-direction.    For    supported    plug-ins    see
                       PLUGINS:1d/spacialkernel

       sws       seeded  watershead.  The  algorithm extracts exactly so many reagions as initial
                 labels are given in the seed image., supported parameters are:

                     grad = 0; bool
                       Interpret the input image as gradient. .

                     mark = 0; bool
                       Mark the segmented watersheds with a special gray scale value.

                     n = [sphere:r=1]; factory
                       Neighborhood for watershead region growing.  For  supported  plug-ins  see
                       PLUGINS:3dimage/shape

                     seed =(input, required, string)
                       seed input image containing the lables for the initial regions.

       tee       Save  the  input  image  to  a file and also pass it through to the next filter,
                 supported parameters are:

                     file =(output, required, string)
                       name of the output file to save the image too..

       thinning  3D morphological thinning,  based on: Lee and Kashyap, 'Building Skeleton Models
                 via  3-D  Medial  Surface/Axis  Thinning Algorithms', Graphical Models and Image
                 Processing, 56(6):462-478,  1994.  This  implementation  only  supports  the  26
                 neighbourhood.

                     (no parameters)

       transform Transform  the  input image with the given transformation., supported parameters
                 are:

                     file =(input, required, string)
                       Name of the file containing the transformation..

                     imgboundary = ; string
                       override image interpolation boundary conditions.

                     imgkernel = ; string
                       override image interpolator kernel.

       variance  3D image variance filter, supported parameters are:

                     w = 1; int in [1, inf)
                       half filter width.

       ws        basic watershead segmentation., supported parameters are:

                     evalgrad = 0; bool
                       Set to 1 if the input image does not represent a gradient norm image.

                     mark = 0; bool
                       Mark the segmented watersheds with a special gray scale value.

                     n = [sphere:r=1]; factory
                       Neighborhood for watershead region growing.  For  supported  plug-ins  see
                       PLUGINS:3dimage/shape

                     thresh = 0; float in [0, 1)
                       Relative  gradient  norm  threshold.  The  actual value threshold value is
                       thresh * (max_grad - min_grad) + min_grad. Bassins separated by  gradients
                       with a lower norm will be joined.

PLUGINS: 3dimage/fullcost

       image     Generalized  image  similarity  cost function that also handles multi-resolution
                 processing.  The  actual  similarity  measure  is  given  es  extra  parameter.,
                 supported parameters are:

                     cost = ssd; factory
                       Cost function kernel.  For supported plug-ins see PLUGINS:3dimage/cost

                     debug = 0; bool
                       Save intermediate resuts for debugging.

                     ref =(input, string)
                       Reference image.

                     src =(input, string)
                       Study image.

                     weight = 1; float
                       weight of cost function.

       labelimage
                 Similarity  cost  function  that  maps  labels  of two images and handles label-
                 preserving multi-resolution processing., supported parameters are:

                     maxlabel = 256; int in [2, 32000]
                       maximum number of labels to consider.

                     ref =(input, string)
                       Reference image.

                     src =(input, string)
                       Study image.

                     weight = 1; float
                       weight of cost function.

       maskedimage
                 Generalized masked image similarity  cost  function  that  also  handles  multi-
                 resolution  processing.  The  provided  masks should be densly filled regions in
                 multi-resolution procesing because otherwise the mask information may  get  lost
                 when  downscaling  the image. The mask may be pre-filtered - after pre-filtering
                 the masks must be of bit-type.The reference mask and the transformed mask of the
                 study  image  are combined by binary AND. The actual similarity measure is given
                 es extra parameter., supported parameters are:

                     cost = ssd; factory
                       Cost     function     kernel.      For     supported     plug-ins      see
                       PLUGINS:3dimage/maskedcost

                     ref =(input, string)
                       Reference image.

                     ref-mask =(input, string)
                       Reference image mask  (binary).

                     ref-mask-filter = ; factory
                       Filter  to  prepare  the reference mask image, the output must be a binary
                       image..  For supported plug-ins see PLUGINS:3dimage/filter

                     src =(input, string)
                       Study image.

                     src-mask =(input, string)
                       Study image mask (binary).

                     src-mask-filter = ; factory
                       Filter to prepare the study mask  image,  the  output  must  be  a  binary
                       image..  For supported plug-ins see PLUGINS:3dimage/filter

                     weight = 1; float
                       weight of cost function.

       taggedssd Evaluates  the  Sum  of  Squared  Differences  similarity measure by using three
                 tagged image pairs. The cost function value is  evaluated  based  on  all  image
                 pairs,  but the gradient is composed by composing its component based on the tag
                 direction., supported parameters are:

                     refx =(input, string)
                       Reference image  X-tag.

                     refy =(input, string)
                       Reference image  Y-tag.

                     refz =(input, string)
                       Reference image  Z-tag.

                     srcx =(input, string)
                       Study image X-tag.

                     srcy =(input, string)
                       Study image Y-tag.

                     srcz =(input, string)
                       Study image Z-tag.

                     weight = 1; float
                       weight of cost function.

PLUGINS: 3dimage/io

       analyze   Analyze 7.5 image

                     Recognized file extensions:  .HDR, .hdr

                     Supported element types:
                       unsigned 8 bit, signed 16 bit, signed  32  bit,  floating  point  32  bit,
                       floating point 64 bit

       datapool  Virtual IO to and from the internal data pool

                     Recognized file extensions:  .@

       dicom     Dicom image series as 3D

                     Recognized file extensions:  .DCM, .dcm

                     Supported element types:
                       signed 16 bit, unsigned 16 bit

       hdf5      HDF5 3D image IO

                     Recognized file extensions:  .H5, .h5

                     Supported element types:
                       binary data, signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit,
                       signed 32 bit, unsigned 32 bit, signed 64 bit, unsigned 64  bit,  floating
                       point 32 bit, floating point 64 bit

       inria     INRIA image

                     Recognized file extensions:  .INR, .inr

                     Supported element types:
                       signed  8  bit,  unsigned 8 bit, signed 16 bit, unsigned 16 bit, signed 32
                       bit, unsigned 32 bit, floating point 32 bit, floating point 64 bit

       mhd       MetaIO 3D image IO using the VTK implementation (experimental).

                     Recognized file extensions:  .MHA, .MHD, .mha, .mhd

                     Supported element types:
                       signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16  bit,  signed  32
                       bit, unsigned 32 bit, floating point 32 bit, floating point 64 bit

       nifti     NIFTI-1 3D image IO

                     Recognized file extensions:  .NII, .nii

                     Supported element types:
                       signed  8  bit,  unsigned 8 bit, signed 16 bit, unsigned 16 bit, signed 32
                       bit, unsigned 32 bit, signed 64 bit, unsigned 64 bit,  floating  point  32
                       bit, floating point 64 bit

       vff       VFF Sun raster format

                     Recognized file extensions:  .VFF, .vff

                     Supported element types:
                       unsigned 8 bit, signed 16 bit

       vista     Vista 3D

                     Recognized file extensions:  .V, .VISTA, .v, .vista

                     Supported element types:
                       binary data, signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit,
                       signed 32 bit, unsigned 32 bit, floating point 32 bit, floating  point  64
                       bit

       vti       3D image VTK-XML in- and output (experimental).

                     Recognized file extensions:  .VTI, .vti

                     Supported element types:
                       signed  8  bit,  unsigned 8 bit, signed 16 bit, unsigned 16 bit, signed 32
                       bit, unsigned 32 bit, floating point 32 bit, floating point 64 bit

       vtk       3D VTK image legacy in- and output (experimental).

                     Recognized file extensions:  .VTK, .VTKIMAGE, .vtk, .vtkimage

                     Supported element types:
                       binary data, signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit,
                       signed  32  bit, unsigned 32 bit, floating point 32 bit, floating point 64
                       bit

PLUGINS: 3dimage/maskedcost

       lncc      local normalized cross correlation with masking support.,  supported  parameters
                 are:

                     w = 5; uint in [1, 256]
                       half  width  of  the  window  used  for  evaluating  the  localized  cross
                       correlation.

       mi        Spline parzen based mutual information with masking., supported parameters are:

                     cut = 0; float in [0, 40]
                       Percentage of pixels  to  cut  at  high  and  low  intensities  to  remove
                       outliers.

                     mbins = 64; uint in [1, 256]
                       Number of histogram bins used for the moving image.

                     mkernel = [bspline:d=3]; factory
                       Spline  kernel for moving image parzen hinstogram.  For supported plug-ins
                       see PLUGINS:1d/splinekernel

                     rbins = 64; uint in [1, 256]
                       Number of histogram bins used for the reference image.

                     rkernel = [bspline:d=0]; factory
                       Spline kernel for reference image parzen hinstogram.  For supported  plug-
                       ins see PLUGINS:1d/splinekernel

       ncc       normalized cross correlation with masking support.

                     (no parameters)

       ssd       Sum of squared differences with masking.

                     (no parameters)

PLUGINS: 3dimage/shape

       18n       18n neighborhood 3D shape creator

                     (no parameters)

       26n       26n neighborhood 3D shape creator

                     (no parameters)

       6n        6n neighborhood 3D shape creator

                     (no parameters)

       sphere    Closed  spherical  shape neighborhood including the pixels within a given radius
                 r., supported parameters are:

                     r = 2; float in (0, inf)
                       sphere radius.

PLUGINS: 3dimage/transform

       affine    Affine transformation (12 degrees of freedom), supported parameters are:

                     imgboundary = mirror; factory
                       image interpolation  boundary  conditions.   For  supported  plug-ins  see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image     interpolator     kernel.     For    supported    plug-ins    see
                       PLUGINS:1d/splinekernel

       axisrot   Restricted rotation transformation (1 degrees of freedom). The transformation is
                 restricted  to  the  rotation  around  the  given  axis about the given rotation
                 center, supported parameters are:

                     axis =(required, 3dfvector)
                       rotation axis.

                     imgboundary = mirror; factory
                       image interpolation  boundary  conditions.   For  supported  plug-ins  see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image     interpolator     kernel.     For    supported    plug-ins    see
                       PLUGINS:1d/splinekernel

                     origin =(required, 3dfvector)
                       center of the transformation.

       raffine   Restricted affine transformation (3 degrees of freedom). The  transformation  is
                 restricted to the rotation around the given axis and shearing along the two axis
                 perpendicular to the given one, supported parameters are:

                     axis =(required, 3dfvector)
                       rotation axis.

                     imgboundary = mirror; factory
                       image interpolation  boundary  conditions.   For  supported  plug-ins  see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image     interpolator     kernel.     For    supported    plug-ins    see
                       PLUGINS:1d/splinekernel

                     origin =(required, 3dfvector)
                       center of the transformation.

       rigid     Rigid transformation, i.e. rotation and translation (six degrees  of  freedom).,
                 supported parameters are:

                     imgboundary = mirror; factory
                       image  interpolation  boundary  conditions.   For  supported  plug-ins see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image    interpolator    kernel.     For    supported     plug-ins     see
                       PLUGINS:1d/splinekernel

                     origin = [[0,0,0]]; 3dfvector
                       Relative rotation center, i.e.  <0.5,0.5,0.5> corresponds to the center of
                       the volume.

       rotation  Rotation transformation (three degrees of freedom)., supported parameters are:

                     imgboundary = mirror; factory
                       image interpolation  boundary  conditions.   For  supported  plug-ins  see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image     interpolator     kernel.     For    supported    plug-ins    see
                       PLUGINS:1d/splinekernel

                     origin = [[0,0,0]]; 3dfvector
                       Relative rotation center, i.e.  <0.5,0.5,0.5> corresponds to the center of
                       the volume.

       rotbend   Restricted   transformation  (4  degrees  of  freedom).  The  transformation  is
                 restricted to the rotation around the x and y axis and a  bending  along  the  x
                 axis,  independedn  in  each  direction,  with  the  bending increasing with the
                 squared distance from the rotation axis., supported parameters are:

                     imgboundary = mirror; factory
                       image interpolation  boundary  conditions.   For  supported  plug-ins  see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image     interpolator     kernel.     For    supported    plug-ins    see
                       PLUGINS:1d/splinekernel

                     norot = 0; bool
                       Don't optimize the rotation.

                     origin =(required, 3dfvector)
                       center of the transformation.

       spline    Free-form transformation that can be described by a set of B-spline coefficients
                 and an underlying B-spline kernel., supported parameters are:

                     anisorate = [[0,0,0]]; 3dfvector
                       anisotropic  coefficient  rate  in  pixels,  nonpositive  values  will  be
                       overwritten by the 'rate' value..

                     debug = 0; bool
                       enable additional debuging output.

                     imgboundary = mirror; factory
                       image interpolation  boundary  conditions.   For  supported  plug-ins  see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image     interpolator     kernel.     For    supported    plug-ins    see
                       PLUGINS:1d/splinekernel

                     kernel = [bspline:d=3]; factory
                       transformation   spline    kernel.     For    supported    plug-ins    see
                       PLUGINS:1d/splinekernel

                     penalty = ; factory
                       transformation   penalty   energy   term.    For  supported  plug-ins  see
                       PLUGINS:3dtransform/splinepenalty

                     rate = 10; float in [1, inf)
                       isotropic coefficient rate in pixels.

       translate Translation (three degrees of freedom), supported parameters are:

                     imgboundary = mirror; factory
                       image interpolation  boundary  conditions.   For  supported  plug-ins  see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image     interpolator     kernel.     For    supported    plug-ins    see
                       PLUGINS:1d/splinekernel

       vf        This plug-in implements a transformation that defines  a  translation  for  each
                 point  of  the  grid  defining  the  domain  of  the  transformation., supported
                 parameters are:

                     imgboundary = mirror; factory
                       image interpolation  boundary  conditions.   For  supported  plug-ins  see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image     interpolator     kernel.     For    supported    plug-ins    see
                       PLUGINS:1d/splinekernel

PLUGINS: 3dtransform/io

       bbs       Binary (non-portable) serialized IO of 3D transformations

                     Recognized file extensions:  .bbs

       datapool  Virtual IO to and from the internal data pool

                     Recognized file extensions:  .@

       vista     Vista storage of 3D transformations

                     Recognized file extensions:  .v, .v3dt

       xml       XML serialized IO of 3D transformations

                     Recognized file extensions:  .x3dt

PLUGINS: 3dtransform/splinepenalty

       divcurl   divcurl penalty on the transformation, supported parameters are:

                     curl = 1; float in [0, inf)
                       penalty weight on curl.

                     div = 1; float in [0, inf)
                       penalty weight on divergence.

                     norm = 0; bool
                       Set to 1 if the penalty should be normalized with  respect  to  the  image
                       size.

                     weight = 1; float in (0, inf)
                       weight of penalty energy.

PLUGINS: minimizer/singlecost

       gdas      Gradient descent with automatic step size correction., supported parameters are:

                     ftolr = 0; double in [0, inf)
                       Stop if the relative change of the criterion is below..

                     max-step = 2; double in (0, inf)
                       Maximal absolute step size.

                     maxiter = 200; uint in [1, inf)
                       Stopping criterion: the maximum number of iterations.

                     min-step = 0.1; double in (0, inf)
                       Minimal absolute step size.

                     xtola = 0.01; double in [0, inf)
                       Stop if the inf-norm of the change applied to x is below this value..

       gdsq      Gradient descent with quadratic step estimation, supported parameters are:

                     ftolr = 0; double in [0, inf)
                       Stop if the relative change of the criterion is below..

                     gtola = 0; double in [0, inf)
                       Stop if the inf-norm of the gradient is below this value..

                     maxiter = 100; uint in [1, inf)
                       Stopping criterion: the maximum number of iterations.

                     scale = 2; double in (1, inf)
                       Fallback fixed step size scaling.

                     step = 0.1; double in (0, inf)
                       Initial step size.

                     xtola = 0; double in [0, inf)
                       Stop if the inf-norm of x-update is below this value..

       gsl       optimizer  plugin  based on the multimin optimizers ofthe GNU Scientific Library
                 (GSL) https://www.gnu.org/software/gsl/, supported parameters are:

                     eps = 0.01; double in (0, inf)
                       gradient based optimizers: stop when |grad|  <  eps,  simplex:  stop  when
                       simplex size < eps..

                     iter = 100; uint in [1, inf)
                       maximum number of iterations.

                     opt = gd; dict
                       Specific optimizer to be used..  Supported values are:
                           bfgs ‐ Broyden-Fletcher-Goldfarb-Shann
                           bfgs2 ‐ Broyden-Fletcher-Goldfarb-Shann (most efficient version)
                           cg-fr ‐ Flecher-Reeves conjugate gradient algorithm
                           gd ‐ Gradient descent.
                           simplex ‐ Simplex algorithm of Nelder and Mead
                           cg-pr ‐ Polak-Ribiere conjugate gradient algorithm

                     step = 0.001; double in (0, inf)
                       initial step size.

                     tol = 0.1; double in (0, inf)
                       some tolerance parameter.

       nlopt     Minimizer  algorithms  using  the  NLOPT  library,  for  a  description  of  the
                 optimizers                please                 see                 'http://ab-
                 initio.mit.edu/wiki/index.php/NLopt_Algorithms', supported parameters are:

                     ftola = 0; double in [0, inf)
                       Stopping  criterion:  the  absolute change of the objective value is below
                       this value.

                     ftolr = 0; double in [0, inf)
                       Stopping criterion: the relative change of the objective  value  is  below
                       this value.

                     higher = inf; double
                       Higher boundary (equal for all parameters).

                     local-opt = none; dict
                       local   minimization   algorithm   that  may  be  required  for  the  main
                       minimization algorithm..  Supported values are:
                           gn-orig-direct-l  ‐  Dividing  Rectangles  (original   implementation,
                           locally biased)
                           gn-direct-l-noscal ‐ Dividing Rectangles (unscaled, locally biased)
                           gn-isres ‐ Improved Stochastic Ranking Evolution Strategy
                           ld-tnewton ‐ Truncated Newton
                           gn-direct-l-rand ‐ Dividing Rectangles (locally biased, randomized)
                           ln-newuoa  ‐ Derivative-free Unconstrained Optimization by Iteratively
                           Constructed Quadratic Approximation
                           gn-direct-l-rand-noscale  ‐  Dividing  Rectangles  (unscaled,  locally
                           biased, randomized)
                           gn-orig-direct ‐ Dividing Rectangles (original implementation)
                           ld-tnewton-precond ‐ Preconditioned Truncated Newton
                           ld-tnewton-restart ‐ Truncated Newton with steepest-descent restarting
                           gn-direct ‐ Dividing Rectangles
                           ln-neldermead ‐ Nelder-Mead simplex algorithm
                           ln-cobyla ‐ Constrained Optimization BY Linear Approximation
                           gn-crs2-lm ‐ Controlled Random Search with Local Mutation
                           ld-var2 ‐ Shifted Limited-Memory Variable-Metric, Rank 2
                           ld-var1 ‐ Shifted Limited-Memory Variable-Metric, Rank 1
                           ld-mma ‐ Method of Moving Asymptotes
                           ld-lbfgs-nocedal ‐ None
                           ld-lbfgs ‐ Low-storage BFGS
                           gn-direct-l ‐ Dividing Rectangles (locally biased)
                           none ‐ don't specify algorithm
                           ln-bobyqa ‐ Derivative-free Bound-constrained Optimization
                           ln-sbplx ‐ Subplex variant of Nelder-Mead
                           ln-newuoa-bound  ‐  Derivative-free  Bound-constrained Optimization by
                           Iteratively Constructed Quadratic Approximation
                           ln-praxis ‐ Gradient-free Local Optimization  via  the  Principal-Axis
                           Method
                           gn-direct-noscal ‐ Dividing Rectangles (unscaled)
                           ld-tnewton-precond-restart  ‐  Preconditioned  Truncated  Newton  with
                           steepest-descent restarting

                     lower = -inf; double
                       Lower boundary (equal for all parameters).

                     maxiter = 100; int in [1, inf)
                       Stopping criterion: the maximum number of iterations.

                     opt = ld-lbfgs; dict
                       main minimization algorithm.  Supported values are:
                           gn-orig-direct-l  ‐  Dividing  Rectangles  (original   implementation,
                           locally biased)
                           g-mlsl-lds  ‐  Multi-Level  Single-Linkage  (low-discrepancy-sequence,
                           require local gradient based optimization and bounds)
                           gn-direct-l-noscal ‐ Dividing Rectangles (unscaled, locally biased)
                           gn-isres ‐ Improved Stochastic Ranking Evolution Strategy
                           ld-tnewton ‐ Truncated Newton
                           gn-direct-l-rand ‐ Dividing Rectangles (locally biased, randomized)
                           ln-newuoa ‐ Derivative-free Unconstrained Optimization by  Iteratively
                           Constructed Quadratic Approximation
                           gn-direct-l-rand-noscale  ‐  Dividing  Rectangles  (unscaled,  locally
                           biased, randomized)
                           gn-orig-direct ‐ Dividing Rectangles (original implementation)
                           ld-tnewton-precond ‐ Preconditioned Truncated Newton
                           ld-tnewton-restart ‐ Truncated Newton with steepest-descent restarting
                           gn-direct ‐ Dividing Rectangles
                           auglag-eq ‐ Augmented Lagrangian algorithm with  equality  constraints
                           only
                           ln-neldermead ‐ Nelder-Mead simplex algorithm
                           ln-cobyla ‐ Constrained Optimization BY Linear Approximation
                           gn-crs2-lm ‐ Controlled Random Search with Local Mutation
                           ld-var2 ‐ Shifted Limited-Memory Variable-Metric, Rank 2
                           ld-var1 ‐ Shifted Limited-Memory Variable-Metric, Rank 1
                           ld-mma ‐ Method of Moving Asymptotes
                           ld-lbfgs-nocedal ‐ None
                           g-mlsl  ‐  Multi-Level  Single-Linkage (require local optimization and
                           bounds)
                           ld-lbfgs ‐ Low-storage BFGS
                           gn-direct-l ‐ Dividing Rectangles (locally biased)
                           ln-bobyqa ‐ Derivative-free Bound-constrained Optimization
                           ln-sbplx ‐ Subplex variant of Nelder-Mead
                           ln-newuoa-bound ‐ Derivative-free  Bound-constrained  Optimization  by
                           Iteratively Constructed Quadratic Approximation
                           auglag ‐ Augmented Lagrangian algorithm
                           ln-praxis  ‐  Gradient-free  Local Optimization via the Principal-Axis
                           Method
                           gn-direct-noscal ‐ Dividing Rectangles (unscaled)
                           ld-tnewton-precond-restart  ‐  Preconditioned  Truncated  Newton  with
                           steepest-descent restarting
                           ld-slsqp ‐ Sequential Least-Squares Quadratic Programming

                     step = 0; double in [0, inf)
                       Initial step size for gradient free methods.

                     stop = -inf; double
                       Stopping criterion: function value falls below this value.

                     xtola = 0; double in [0, inf)
                       Stopping  criterion:  the  absolute  change of all x-values is below  this
                       value.

                     xtolr = 0; double in [0, inf)
                       Stopping criterion: the relative change of all  x-values  is  below   this
                       value.

EXAMPLE

       Register  the  image  series  given  by  images  imageXXXX.v  by optimizing a spline based
       transformation with a coefficient rate of 16 pixel, skipping two images at  the  beginning
       and  using  normalized  gradient  fields as initial cost measure and SSD as final measure.
       Penalize the transformation by using divcurl with aweight of 2.0. As  optimizer  an  nlopt
       based newton method is used.

       mia-3dprealign-nonrigid  mia-3dprealign-nonrigid  -i imageXXXX.v -o registered -t vista -k
              2-F  spline:rate=16,penalty=[divcurl:weight=2]   -1   image:cost=[ngf:eval=ds]   -2
              image:cost=ssd -O nlopt:opt=ld-var1,xtola=0.001,ftolr=0.001,maxiter=300

AUTHOR(s)

       Gert Wollny

COPYRIGHT

       This  software  is  Copyright  (c) 1999‐2015 Leipzig, Germany and Madrid, Spain.  It comes
       with  ABSOLUTELY  NO WARRANTY  and  you  may redistribute it under the terms  of  the  GNU
       GENERAL PUBLIC LICENSE Version 3 (or later). For more information run the program with the
       option '--copyright'.