Evaluate the similarity between two 2d images.
mia-2dcost [options] <PLUGINS:2dimage/fullcost>
mia-2dcost This program is used to evaluate the cost between two images by using a given cost function.
verbosity of output, print messages of given level and higher priorities. Supported priorities starting at lowest level are:
info \(hy Low level messages
trace \(hy Function call trace
fail \(hy Report test failures
warning \(hy Warnings
error \(hy Report errors
debug \(hy Debug output
message \(hy Normal messages
fatal \(hy Report only fatal errors
print copyright information
print this help
print a short help
print the version number and exit
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).
bspline
B-spline kernel creation , supported parameters are:
d = 3 (int)
Spline degree. in [0, 5]
omoms
OMoms-spline kernel creation, supported parameters are:
d = 3 (int)
Spline degree. in [3, 3]
lncc
local normalized cross correlation with masking support., supported parameters are:
w = 5 (uint)
half width of the window used for evaluating the localized cross correlation. in [1, 256]
lsd
Least-Squares Distance measure
(no parameters)
mi
Spline parzen based mutual information., supported parameters are:
cut = 0 (float)
Percentage of pixels to cut at high and low intensities to remove outliers. in [0, 40]
mbins = 64 (uint)
Number of histogram bins used for the moving image. in [1, 256]
mkernel = [bspline:d=3] (factory)
Spline kernel for moving image parzen hinstogram. For supported plug-ins see PLUGINS:1d/splinekernel
rbins = 64 (uint)
Number of histogram bins used for the reference image. in [1, 256]
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 \(hy square of difference
ds \(hy square of scaled difference
dot \(hy scalar product kernel
cross \(hy cross product kernel
ssd
2D imaga cost: sum of squared differences, supported parameters are:
autothresh = 0 (float)
Use automatic masking of the moving image by only takeing intensity values into accound that are larger than the given threshold. in [0, 1000]
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. in [-1.79769e+308, 1.79769e+308]
sthresh = 0 (double)
Threshold intensity value for source image. in [-1.79769e+308, 1.79769e+308]
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. in [-1e+10, 1e+10]
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. in [-1e+10, 1e+10]
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
lncc
local normalized cross correlation with masking support., supported parameters are:
w = 5 (uint)
half width of the window used for evaluating the localized cross correlation. in [1, 256]
mi
Spline parzen based mutual information with masking., supported parameters are:
cut = 0 (float)
Percentage of pixels to cut at high and low intensities to remove outliers. in [0, 40]
mbins = 64 (uint)
Number of histogram bins used for the moving image. in [1, 256]
mkernel = [bspline:d=3] (factory)
Spline kernel for moving image parzen hinstogram. For supported plug-ins see PLUGINS:1d/splinekernel
rbins = 64 (uint)
Number of histogram bins used for the reference image. in [1, 256]
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)
Evaluate the SSD cost function between image1.png and image2.png mia-2dcost image:src=image1.png,ref=image2.png,cost=ssd
Gert Wollny
This software is Copyright (c) 1999\(hy2013 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'.