SYNOPSIS
mia-2dimageregistration -i <in-image> -r <ref-image> -t <transformation> [options] <PLUGINS:2dimage/fullcost>DESCRIPTION
mia-2dimageregistration This program runs registration of two images optimizing a transformation of the given transformation model by optimizing certain cost measures that are given as free parameters.OPTIONS
File-IO
-
- -i --in-image=(input, required); io
- test image to be registered For supported file types see PLUGINS:2dimage/io
- -r --ref-image=(input, required); io
- reference image to be registered to For supported file types see PLUGINS:2dimage/io
- -o --out-image=(output); io
- registered output image For supported file types see PLUGINS:2dimage/io
- -t --transformation=(output, required); io
- output transformation comprising the registration For supported file types see PLUGINS:2dtransform/io
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
Parameters
-
- -l --levels=3
- multi-resolution levelsmulti-resolution levels
- -O --optimizer=gsl:opt=gd,step=0.1
- Optimizer used for minimizationOptimizer used for minimization For supported plugins see PLUGINS:minimizer/singlecost
- -R --refiner=
- optimizer used for refinement after the main optimizer was calledoptimizer used for refinement after the main optimizer was called For supported plugins see PLUGINS:minimizer/singlecost
- -f --transForm=spline
- transformation typetransformation type For supported plugins see PLUGINS:2dimage/transform
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/splinebc
- mirror
- Spline interpolation boundary conditions that mirror on the boundary
- (no parameters)
- repeat
- Spline interpolation boundary conditions that repeats the value at the boundary
- (no parameters)
- zero
- Spline interpolation boundary conditions that assumes zero for values outside
- (no parameters)
PLUGINS: 1d/splinekernel
- bspline
- B-spline kernel creation , supported parameters are:
-
d
= 3; int in [0, 5]
-
Spline degree.
-
Spline degree.
- omoms
- OMoms-spline kernel creation, supported parameters are:
-
d
= 3; int in [3, 3]
-
Spline degree.
-
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.
-
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.
-
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.
-
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
-
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.
-
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
-
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
-
plugin subtype.
Supported values are:
- 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.
-
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.
-
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.
-
Threshold intensity value for reference image.
-
sthresh
= 0; double
-
Threshold intensity value for source image.
-
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
-
Cost function kernel.
For supported plug-ins see PLUGINS:2dimage/cost
-
debug
= 0; bool
-
Save intermediate resuts for debugging.
-
Save intermediate resuts for debugging.
-
ref
=(input, string)
-
Reference image.
-
Reference image.
-
src
=(input, string)
-
Study image.
-
Study image.
-
weight
= 1; float
-
weight of cost function.
-
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.
-
write the distance transforms to a 3D image.
-
maxlabel
= 256; int in [2, 32000]
-
maximum number of labels to consider.
-
maximum number of labels to consider.
-
ref
=(input, string)
-
Reference image.
-
Reference image.
-
src
=(input, string)
-
Study image.
-
Study image.
-
weight
= 1; float
-
weight of cost function.
-
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
-
Cost function kernel.
For supported plug-ins see PLUGINS:2dimage/maskedcost
-
ref
=(input, string)
-
Reference image.
-
Reference image.
-
ref-mask
=(input, string)
-
Reference image mask (binary).
-
Reference image mask (binary).
-
src
=(input, string)
-
Study image.
-
Study image.
-
src-mask
=(input, string)
-
Study image mask (binary).
-
Study image mask (binary).
-
weight
= 1; float
-
weight of cost function.
-
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.
-
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.
-
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.
-
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
-
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.
-
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
-
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)
PLUGINS: 2dimage/transform
- affine
- Affine transformation (six degrees of freedom)., supported parameters are:
-
imgboundary
= mirror; factory
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
imgkernel
= [bspline:d=3]; factory
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
- rigid
- Rigid transformations (i.e. rotation and translation, three degrees of freedom)., supported parameters are:
-
imgboundary
= mirror; factory
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
imgkernel
= [bspline:d=3]; factory
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
-
rot-center
= [[0,0]]; 2dfvector
-
Relative rotation center, i.e. <0.5,0.5> corresponds to the center of the support rectangle.
-
Relative rotation center, i.e. <0.5,0.5> corresponds to the center of the support rectangle.
- rotation
- Rotation transformations (i.e. rotation about a given center, one degree of freedom)., supported parameters are:
-
imgboundary
= mirror; factory
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
imgkernel
= [bspline:d=3]; factory
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
-
rot-center
= [[0,0]]; 2dfvector
-
Relative rotation center, i.e. <0.5,0.5> corresponds to the center of the support rectangle.
-
Relative rotation center, i.e. <0.5,0.5> corresponds to the center of the support rectangle.
- spline
- Free-form transformation that can be described by a set of B-spline coefficients and an underlying B-spline kernel., supported parameters are:
-
anisorate
= [[0,0]]; 2dfvector
-
anisotropic coefficient rate in pixels, nonpositive values will be overwritten by the 'rate' value..
-
anisotropic coefficient rate in pixels, nonpositive values will be overwritten by the 'rate' value..
-
imgboundary
= mirror; factory
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
imgkernel
= [bspline:d=3]; factory
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
-
kernel
= [bspline:d=3]; factory
-
transformation spline kernel..
For supported plug-ins see PLUGINS:1d/splinekernel
-
transformation spline kernel..
For supported plug-ins see PLUGINS:1d/splinekernel
-
penalty
= ; factory
-
Transformation penalty term.
For supported plug-ins see PLUGINS:2dtransform/splinepenalty
-
Transformation penalty term.
For supported plug-ins see PLUGINS:2dtransform/splinepenalty
-
rate
= 10; float in [1, inf)
-
isotropic coefficient rate in pixels.
-
isotropic coefficient rate in pixels.
- translate
- Translation only (two degrees of freedom), supported parameters are:
-
imgboundary
= mirror; factory
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
imgkernel
= [bspline:d=3]; factory
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
- vf
- This plug-in implements a transformation that defines a translation for each point of the grid defining the domain of the transformation., supported parameters are:
-
imgboundary
= mirror; factory
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
image interpolation boundary conditions.
For supported plug-ins see PLUGINS:1d/splinebc
-
imgkernel
= [bspline:d=3]; factory
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
-
image interpolator kernel.
For supported plug-ins see PLUGINS:1d/splinekernel
PLUGINS: 2dtransform/io
- bbs
- Binary (non-portable) serialized IO of 2D transformations
- Recognized file extensions: .bbs
- datapool
- Virtual IO to and from the internal data pool
- Recognized file extensions: .@
- vista
- Vista storage of 2D transformations
- Recognized file extensions: .v2dt
- xml
- XML serialized IO of 2D transformations
- Recognized file extensions: .x2dt
PLUGINS: 2dtransform/splinepenalty
- divcurl
- divcurl penalty on the transformation, supported parameters are:
-
curl
= 1; float in [0, inf)
-
penalty weight on curl.
-
penalty weight on curl.
-
div
= 1; float in [0, inf)
-
penalty weight on divergence.
-
penalty weight on divergence.
-
norm
= 0; bool
-
Set to 1 if the penalty should be normalized with respect to the image size.
-
Set to 1 if the penalty should be normalized with respect to the image size.
-
weight
= 1; float in (0, inf)
-
weight of penalty energy.
-
weight of penalty energy.
PLUGINS: minimizer/singlecost
- gdas
- Gradient descent with automatic step size correction., supported parameters are:
-
ftolr
= 0; double in [0, inf)
-
Stop if the relative change of the criterion is below..
-
Stop if the relative change of the criterion is below..
-
max-step
= 2; double in (0, inf)
-
Maximal absolute step size.
-
Maximal absolute step size.
-
maxiter
= 200; uint in [1, inf)
-
Stopping criterion: the maximum number of iterations.
-
Stopping criterion: the maximum number of iterations.
-
min-step
= 0.1; double in (0, inf)
-
Minimal absolute step size.
-
Minimal absolute step size.
-
xtola
= 0.01; double in [0, inf)
-
Stop if the inf-norm of the change applied to x is below this value..
-
Stop if the inf-norm of the change applied to x is below this value..
- gdsq
- Gradient descent with quadratic step estimation, supported parameters are:
-
ftolr
= 0; double in [0, inf)
-
Stop if the relative change of the criterion is below..
-
Stop if the relative change of the criterion is below..
-
gtola
= 0; double in [0, inf)
-
Stop if the inf-norm of the gradient is below this value..
-
Stop if the inf-norm of the gradient is below this value..
-
maxiter
= 100; uint in [1, inf)
-
Stopping criterion: the maximum number of iterations.
-
Stopping criterion: the maximum number of iterations.
-
scale
= 2; double in (1, inf)
-
Fallback fixed step size scaling.
-
Fallback fixed step size scaling.
-
step
= 0.1; double in (0, inf)
-
Initial step size.
-
Initial step size.
-
xtola
= 0; double in [0, inf)
-
Stop if the inf-norm of x-update is below this value..
-
Stop if the inf-norm of x-update is below this value..
- gsl
- optimizer plugin based on the multimin optimizers ofthe GNU Scientific Library (GSL) https://www.gnu.org/software/gsl/, supported parameters are:
-
eps
= 0.01; double in (0, inf)
-
gradient based optimizers: stop when |grad| < eps, simplex: stop when simplex size < eps..
-
gradient based optimizers: stop when |grad| < eps, simplex: stop when simplex size < eps..
-
iter
= 100; uint in [1, inf)
-
maximum number of iterations.
-
maximum number of iterations.
-
opt
= gd; dict
-
Specific optimizer to be used..
Supported values are:
- bfgs - Broyden-Fletcher-Goldfarb-Shann
- bfgs2 - Broyden-Fletcher-Goldfarb-Shann (most efficient version)
- cg-fr - Flecher-Reeves conjugate gradient algorithm
- gd - Gradient descent.
- simplex - Simplex algorithm of Nelder and Mead
- cg-pr - Polak-Ribiere conjugate gradient algorithm
-
Specific optimizer to be used..
Supported values are:
-
step
= 0.001; double in (0, inf)
-
initial step size.
-
initial step size.
-
tol
= 0.1; double in (0, inf)
-
some tolerance parameter.
-
some tolerance parameter.
- nlopt
- Minimizer algorithms using the NLOPT library, for a description of the optimizers please see 'http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms', supported parameters are:
-
ftola
= 0; double in [0, inf)
-
Stopping criterion: the absolute change of the objective value is below this value.
-
Stopping criterion: the absolute change of the objective value is below this value.
-
ftolr
= 0; double in [0, inf)
-
Stopping criterion: the relative change of the objective value is below this value.
-
Stopping criterion: the relative change of the objective value is below this value.
-
higher
= inf; double
-
Higher boundary (equal for all parameters).
-
Higher boundary (equal for all parameters).
-
local-opt
= none; dict
-
local minimization algorithm that may be required for the main minimization algorithm..
Supported values are:
- gn-orig-direct-l - Dividing Rectangles (original implementation, locally biased)
- gn-direct-l-noscal - Dividing Rectangles (unscaled, locally biased)
- gn-isres - Improved Stochastic Ranking Evolution Strategy
- ld-tnewton - Truncated Newton
- gn-direct-l-rand - Dividing Rectangles (locally biased, randomized)
- ln-newuoa - Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation
- gn-direct-l-rand-noscale - Dividing Rectangles (unscaled, locally biased, randomized)
- gn-orig-direct - Dividing Rectangles (original implementation)
- ld-tnewton-precond - Preconditioned Truncated Newton
- ld-tnewton-restart - Truncated Newton with steepest-descent restarting
- gn-direct - Dividing Rectangles
- ln-neldermead - Nelder-Mead simplex algorithm
- ln-cobyla - Constrained Optimization BY Linear Approximation
- gn-crs2-lm - Controlled Random Search with Local Mutation
- ld-var2 - Shifted Limited-Memory Variable-Metric, Rank 2
- ld-var1 - Shifted Limited-Memory Variable-Metric, Rank 1
- ld-mma - Method of Moving Asymptotes
- ld-lbfgs-nocedal - None
- ld-lbfgs - Low-storage BFGS
- gn-direct-l - Dividing Rectangles (locally biased)
- none - don't specify algorithm
- ln-bobyqa - Derivative-free Bound-constrained Optimization
- ln-sbplx - Subplex variant of Nelder-Mead
- ln-newuoa-bound - Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation
- ln-praxis - Gradient-free Local Optimization via the Principal-Axis Method
- gn-direct-noscal - Dividing Rectangles (unscaled)
- ld-tnewton-precond-restart - Preconditioned Truncated Newton with steepest-descent restarting
-
local minimization algorithm that may be required for the main minimization algorithm..
Supported values are:
-
lower
= -inf; double
-
Lower boundary (equal for all parameters).
-
Lower boundary (equal for all parameters).
-
maxiter
= 100; int in [1, inf)
-
Stopping criterion: the maximum number of iterations.
-
Stopping criterion: the maximum number of iterations.
-
opt
= ld-lbfgs; dict
-
main minimization algorithm.
Supported values are:
- gn-orig-direct-l - Dividing Rectangles (original implementation, locally biased)
- g-mlsl-lds - Multi-Level Single-Linkage (low-discrepancy-sequence, require local gradient based optimization and bounds)
- gn-direct-l-noscal - Dividing Rectangles (unscaled, locally biased)
- gn-isres - Improved Stochastic Ranking Evolution Strategy
- ld-tnewton - Truncated Newton
- gn-direct-l-rand - Dividing Rectangles (locally biased, randomized)
- ln-newuoa - Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation
- gn-direct-l-rand-noscale - Dividing Rectangles (unscaled, locally biased, randomized)
- gn-orig-direct - Dividing Rectangles (original implementation)
- ld-tnewton-precond - Preconditioned Truncated Newton
- ld-tnewton-restart - Truncated Newton with steepest-descent restarting
- gn-direct - Dividing Rectangles
- auglag-eq - Augmented Lagrangian algorithm with equality constraints only
- ln-neldermead - Nelder-Mead simplex algorithm
- ln-cobyla - Constrained Optimization BY Linear Approximation
- gn-crs2-lm - Controlled Random Search with Local Mutation
- ld-var2 - Shifted Limited-Memory Variable-Metric, Rank 2
- ld-var1 - Shifted Limited-Memory Variable-Metric, Rank 1
- ld-mma - Method of Moving Asymptotes
- ld-lbfgs-nocedal - None
- g-mlsl - Multi-Level Single-Linkage (require local optimization and bounds)
- ld-lbfgs - Low-storage BFGS
- gn-direct-l - Dividing Rectangles (locally biased)
- ln-bobyqa - Derivative-free Bound-constrained Optimization
- ln-sbplx - Subplex variant of Nelder-Mead
- ln-newuoa-bound - Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation
- auglag - Augmented Lagrangian algorithm
- ln-praxis - Gradient-free Local Optimization via the Principal-Axis Method
- gn-direct-noscal - Dividing Rectangles (unscaled)
- ld-tnewton-precond-restart - Preconditioned Truncated Newton with steepest-descent restarting
- ld-slsqp - Sequential Least-Squares Quadratic Programming
-
main minimization algorithm.
Supported values are:
-
step
= 0; double in [0, inf)
-
Initial step size for gradient free methods.
-
Initial step size for gradient free methods.
-
stop
= -inf; double
-
Stopping criterion: function value falls below this value.
-
Stopping criterion: function value falls below this value.
-
xtola
= 0; double in [0, inf)
-
Stopping criterion: the absolute change of all x-values is below this value.
-
Stopping criterion: the absolute change of all x-values is below this value.
-
xtolr
= 0; double in [0, inf)
-
Stopping criterion: the relative change of all x-values is below this value.
-
Stopping criterion: the relative change of all x-values is below this value.
EXAMPLE
Register the image 'moving.png' to the image 'reference.png' by using a rigid transformation model and ssd as cost function. Write the result to output.png- mia-2dimageregistration -i moving.png -r reference.png -o output.png -f rigid image:cost=ssd
AUTHOR(s)
Gert WollnyCOPYRIGHT
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'.