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