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'.