mia-2dcost - 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.
- -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
- --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).
- bspline
- B-spline kernel creation , supported parameters are:
- omoms
- OMoms-spline kernel creation, supported parameters are:
- 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.
- 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
- 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
- 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
- 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
- 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)
Evaluate the SSD cost function between image1.png and
image2.png
mia-2dcost image:src=image1.png,ref=image2.png,cost=ssd
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'.