xenial (1) mia-2dmulti-force.1.gz

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

NAME

       mia-2dmulti-force - Registration force between two images

SYNOPSIS

       mia-2dmulti-force -o <out-file> [options] <PLUGINS:2dimage/fullcost>

DESCRIPTION

       mia-2dmulti-force This program evaluates the 2D image cost force norm image of a given cost function set.
       The input images must be of the same dimensions and gray scale (whatever bit-depth).

OPTIONS

              -o --out-file=(output, required); io
                     output norm image  For supported file types see PLUGINS:2dimage/io

   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/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 availabe., 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, 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:

                     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, 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 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, string)
                       Reference image.

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

                     src =(input, string)
                       Study image.

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

                     weight = 1; float
                       weight of cost function.

PLUGINS: 2dimage/io

       bmp       BMP 2D-image input/output support

                     Recognized file extensions:  .BMP, .bmp

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

EXAMPLE

       Evaluate  the  force  normimage weighted sum of costs SSD and NGF of image1.v and image2.v. and store the
       result to force.v.

       mia-2dmulti-force -o force.v
               image:cost=ssd,src=image1.v,ref=image2.v,weight=0.1
               image:cost=ngf,src=image1.v,ref=image2.v,weight=2.0

AUTHOR(s)

       Gert Wollny

       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'.