Provided by: mia-tools_2.4.6-5build3_amd64 bug

NAME

       mia-2dcost - Evaluate the similarity between two 2D images.

SYNOPSIS

       mia-2dcost [options] <PLUGINS:2dimage/fullcost>

DESCRIPTION

       mia-2dcost This program is used to evaluate the cost between two images by using a given cost function.

OPTIONS

   Help & Info
              -V --verbose=warning
                     verbosity  of  output,  print  messages  of  given  level  and higher priorities. Supported
                     priorities starting at lowest level are:

                        trace ‐ Function call trace
                        debug ‐ Debug output
                        info ‐ Low level messages
                        message ‐ Normal messages
                        warning ‐ Warnings
                        fail ‐ Report test failures
                        error ‐ Report errors
                        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/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)

EXAMPLE

       Evaluate the SSD cost function between image1.png and image2.png

       mia-2dcost image:src=image1.png,ref=image2.png,cost=ssd

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'.