Provided by: mia-tools_2.4.6-4ubuntu2_amd64 bug

NAME

       ('mia\-2dmultiimageregistration',) - Non-linear registration of 2D images.

SYNOPSIS

       mia-2dmultiimageregistration -o <out-transform> [options] <PLUGINS:2dimage/fullcost>

DESCRIPTION

       mia-2dmultiimageregistration This program runs a non-rigid registration based on the given
       cost criteria and a given transformation model. Other than  mia-2dnonrigidreg  it  doesn't
       support  specific  command  line  parameters to provide the images. Instead the images are
       specified dirctly when defining the cost  function.  Hence,  image  registrations  can  be
       executed  that  optimize the aligmnet of  more than one image pair at the same time. Note,
       however, that all input images must be of the same dimension (in pixels)

OPTIONS

              -o --out-transform=(output, required); io
                     output transformation
                      For supported file types see PLUGINS:2dtransform/io

              -l --levels=3
                     multi-resolution levels

              -O --optimizer=gsl:opt=gd,step=0.1
                     Optimizer used for minimization
                      For supported plugins see PLUGINS:minimizer/singlecost

              -f --transForm=spline:rate=10,penalty=divcurl
                     transformation type
                      For supported plugins see PLUGINS:2dimage/transform

   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).

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: 2dimage/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.

       lsd       Least-Squares Distance measure

                     (no parameters)

       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. Various evaluation kernels are available., supported parameters are:

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

       ssd       2D imaga 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
                 2D 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: 2dimage/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:2dimage/cost

                     debug = 0; bool
                       Save intermediate resuts for debugging.

                     ref =(input, io)
                       Reference image.  For supported file types see PLUGINS:2dimage/io

                     src =(input, io)
                       Study image.  For supported file types see PLUGINS:2dimage/io

                     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:

                     debug = 0; int in [0, 1]
                       write the distance transforms to a 3D image.

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

                     ref =(input, io)
                       Reference image.  For supported file types see PLUGINS:2dimage/io

                     src =(input, io)
                       Study image.  For supported file types see PLUGINS:2dimage/io

                     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 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:2dimage/maskedcost

                     ref =(input, io)
                       Reference image.  For supported file types see PLUGINS:2dimage/io

                     ref-mask =(input, io)
                       Reference  image  mask   (binary).    For   supported   file   types   see
                       PLUGINS:2dimage/io

                     src =(input, io)
                       Study image.  For supported file types see PLUGINS:2dimage/io

                     src-mask =(input, io)
                       Study    image    mask   (binary).    For   supported   file   types   see
                       PLUGINS:2dimage/io

                     weight = 1; float
                       weight of cost function.

PLUGINS: 2dimage/io

       bmp       BMP 2D-image input/output support. The plug-in supports reading and  writing  of
                 binary  images  and  8-bit  gray scale images. read-only support is provided for
                 4-bit gray scale images. The color table is ignored and  the  pixel  values  are
                 taken as literal gray scale values.

                     ('Recognized file extensions: ', '.BMP, .bmp')

                     Supported element types:
                       binary data, unsigned 8 bit

       datapool  Virtual IO to and from the internal data pool

                     ('Recognized file extensions: ', '.@')

       dicom     2D image io for DICOM

                     ('Recognized file extensions: ', '.DCM, .dcm')

                     Supported element types:
                       signed 16 bit, unsigned 16 bit

       exr       a 2dimage io plugin for OpenEXR images

                     ('Recognized file extensions: ', '.EXR, .exr')

                     Supported element types:
                       unsigned 32 bit, floating point 32 bit

       jpg       a 2dimage io plugin for jpeg gray scale images

                     ('Recognized file extensions: ', '.JPEG, .JPG, .jpeg, .jpg')

                     Supported element types:
                       unsigned 8 bit

       png       a 2dimage io plugin for png images

                     ('Recognized file extensions: ', '.PNG, .png')

                     Supported element types:
                       binary data, unsigned 8 bit, unsigned 16 bit

       raw       RAW 2D-image output support

                     ('Recognized file extensions: ', '.RAW, .raw')

                     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

       tif       TIFF 2D-image input/output support

                     ('Recognized file extensions: ', '.TIF, .TIFF, .tif, .tiff')

                     Supported element types:
                       binary data, unsigned 8 bit, unsigned 16 bit, unsigned 32 bit

       vista     a 2dimage io plugin for vista images

                     ('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

PLUGINS: 2dimage/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: 2dimage/transform

       affine    Affine transformation (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

       rigid     Rigid  transformations  (i.e.  rotation  and  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

                     rot-center = [[0,0]]; 2dfvector
                       Relative rotation center, i.e.  <0.5,0.5> corresponds to the center of the
                       support rectangle.

       rotation  Rotation transformations (i.e. rotation about a  given  center,  one  degree  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

                     rot-center = [[0,0]]; 2dfvector
                       Relative rotation center, i.e.  <0.5,0.5> corresponds to the center of the
                       support rectangle.

       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]]; 2dfvector
                       anisotropic  coefficient  rate  in  pixels,  nonpositive  values  will  be
                       overwritten by the 'rate' value..

                     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    term.     For    supported    plug-ins    see
                       PLUGINS:2dtransform/splinepenalty

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

       translate Translation only (two 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: 2dtransform/io

       bbs       Binary (non-portable) serialized IO of 2D transformations

                     ('Recognized file extensions: ', '.bbs')

       datapool  Virtual IO to and from the internal data pool

                     ('Recognized file extensions: ', '.@')

       vista     Vista storage of 2D transformations

                     ('Recognized file extensions: ', '.v2dt')

       xml       XML serialized IO of 2D transformations

                     ('Recognized file extensions: ', '.x2dt')

PLUGINS: 2dtransform/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 of the 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  image  test.v to image ref.v by using a spline transformation with a coefficient
       rate of 5  and write the registered image to reg.v. Use two multiresolution levels, ssd as
       image cost function and divcurl weighted by 10.0 as transformation smoothness penalty. The
       resulting transformation is saved in reg.vf.

       mia-2dmultiimageregistration -o reg.vf -l 2
               -f spline:rate=3,penalty=divcurl
               image:cost=ssd,src=test.v,ref=ref.v

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'.