Run a 2d image registration.
mia-2dimageregistration -i <in> -r <ref> -t <trans> [options] <PLUGINS:2dimage/fullcost>
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.
test image For supported file types see PLUGINS:2dimage/io
reference image For supported file types see PLUGINS:2dimage/io
registered output image For supported file types see PLUGINS:2dimage/io
output transformation For supported file types see PLUGINS:2dtransform/io
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
multi-resolution levels
Optimizer used for minimization For supported plugins see PLUGINS:minimizer/singlecost
optimizer used for refinement after the main optimizer was called For supported plugins see PLUGINS:minimizer/singlecost
transformation type For supported plugins see PLUGINS:2dimage/transform
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).
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)
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)
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
imgkernel = [bspline:d=3] (factory)
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
imgkernel = [bspline:d=3] (factory)
image interpolator kernel. For supported plug-ins see PLUGINS:1d/splinekernel
rot-center = [[0,0]] (streamable)
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
imgkernel = [bspline:d=3] (factory)
image interpolator kernel. For supported plug-ins see PLUGINS:1d/splinekernel
rot-center = [[0,0]] (streamable)
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..
imgboundary = mirror (factory)
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
kernel = [bspline:d=3] (factory)
transformation spline kernel.. For supported plug-ins see PLUGINS:1d/splinekernel
penalty = (factory)
Transformation penalty term. For supported plug-ins see PLUGINS:2dtransform/splinepenalty
rate = 10 (float)
isotropic coefficient rate in pixels. in [1, 3.40282e+38]
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
imgkernel = [bspline:d=3] (factory)
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
imgkernel = [bspline:d=3] (factory)
image interpolator kernel. For supported plug-ins see PLUGINS:1d/splinekernel
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
divcurl
divcurl penalty on the transformation, supported parameters are:
curl = 1 (float)
penalty weight on curl. in [0, 3.40282e+38]
div = 1 (float)
penalty weight on divergence. in [0, 3.40282e+38]
norm = 0 (bool)
Set to 1 if the penalty should be normalized with respect to the image size.
weight = 1 (float)
weight of penalty energy. in [0, 3.40282e+38]
gdas
Gradient descent with automatic step size correction., supported parameters are:
ftolr = 0 (double)
Stop if the relative change of the criterion is below.. in [0, INF]
max-step = 2 (double)
Minimal absolute step size. in [1, INF]
maxiter = 200 (uint)
Stopping criterion: the maximum number of iterations. in [1, 2147483647]
min-step = 0.1 (double)
Maximal absolute step size. in [1e-10, INF]
xtola = 0.01 (double)
Stop if the inf-norm of the change applied to x is below this value.. in [0, INF]
gdsq
Gradient descent with quadratic step estimation, supported parameters are:
ftolr = 0 (double)
Stop if the relative change of the criterion is below.. in [0, INF]
gtola = 0 (double)
Stop if the inf-norm of the gradient is below this value.. in [0, INF]
maxiter = 100 (uint)
Stopping criterion: the maximum number of iterations. in [1, 2147483647]
scale = 2 (double)
Fallback fixed step size scaling. in [1, INF]
step = 0.1 (double)
Initial step size. in [0, INF]
xtola = 0 (double)
Stop if the inf-norm of x-update is below this value.. in [0, INF]
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)
gradient based optimizers: stop when |grad| < eps, simplex: stop when simplex size < eps.. in [1e-10, 10]
iter = 100 (int)
maximum number of iterations. in [1, 2147483647]
opt = gd (dict)
Specific optimizer to be used.. Supported values are:
bfgs \(hy Broyden-Fletcher-Goldfarb-Shann
bfgs2 \(hy Broyden-Fletcher-Goldfarb-Shann (most efficient version)
cg-fr \(hy Flecher-Reeves conjugate gradient algorithm
gd \(hy Gradient descent.
simplex \(hy Simplex algorithm of Nelder and Mead
cg-pr \(hy Polak-Ribiere conjugate gradient algorithm
step = 0.001 (double)
initial step size. in [0, 10]
tol = 0.1 (double)
some tolerance parameter. in [0.001, 10]
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)
Stopping criterion: the absolute change of the objective value is below this value. in [0, INF]
ftolr = 0 (double)
Stopping criterion: the relative change of the objective value is below this value. in [0, INF]
higher = inf (double)
Higher boundary (equal for all parameters). in [INF, INF]
local-opt = none (dict)
local minimization algorithm that may be required for the main minimization algorithm.. Supported values are:
gn-orig-direct-l \(hy Dividing Rectangles (original implementation, locally biased)
gn-direct-l-noscal \(hy Dividing Rectangles (unscaled, locally biased)
gn-isres \(hy Improved Stochastic Ranking Evolution Strategy
ld-tnewton \(hy Truncated Newton
gn-direct-l-rand \(hy Dividing Rectangles (locally biased, randomized)
ln-newuoa \(hy Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation
gn-direct-l-rand-noscale \(hy Dividing Rectangles (unscaled, locally biased, randomized)
gn-orig-direct \(hy Dividing Rectangles (original implementation)
ld-tnewton-precond \(hy Preconditioned Truncated Newton
ld-tnewton-restart \(hy Truncated Newton with steepest-descent restarting
gn-direct \(hy Dividing Rectangles
ln-neldermead \(hy Nelder-Mead simplex algorithm
ln-cobyla \(hy Constrained Optimization BY Linear Approximation
gn-crs2-lm \(hy Controlled Random Search with Local Mutation
ld-var2 \(hy Shifted Limited-Memory Variable-Metric, Rank 2
ld-var1 \(hy Shifted Limited-Memory Variable-Metric, Rank 1
ld-mma \(hy Method of Moving Asymptotes
ld-lbfgs-nocedal \(hy None
ld-lbfgs \(hy Low-storage BFGS
gn-direct-l \(hy Dividing Rectangles (locally biased)
none \(hy don't specify algorithm
ln-bobyqa \(hy Derivative-free Bound-constrained Optimization
ln-sbplx \(hy Subplex variant of Nelder-Mead
ln-newuoa-bound \(hy Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation
ln-praxis \(hy Gradient-free Local Optimization via the Principal-Axis Method
gn-direct-noscal \(hy Dividing Rectangles (unscaled)
ld-tnewton-precond-restart \(hy Preconditioned Truncated Newton with steepest-descent restarting
lower = -inf (double)
Lower boundary (equal for all parameters). in [INF, INF]
maxiter = 100 (int)
Stopping criterion: the maximum number of iterations. in [1, 2147483647]
opt = ld-lbfgs (dict)
main minimization algorithm. Supported values are:
gn-orig-direct-l \(hy Dividing Rectangles (original implementation, locally biased)
g-mlsl-lds \(hy Multi-Level Single-Linkage (low-discrepancy-sequence, require local gradient based optimization and bounds)
gn-direct-l-noscal \(hy Dividing Rectangles (unscaled, locally biased)
gn-isres \(hy Improved Stochastic Ranking Evolution Strategy
ld-tnewton \(hy Truncated Newton
gn-direct-l-rand \(hy Dividing Rectangles (locally biased, randomized)
ln-newuoa \(hy Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation
gn-direct-l-rand-noscale \(hy Dividing Rectangles (unscaled, locally biased, randomized)
gn-orig-direct \(hy Dividing Rectangles (original implementation)
ld-tnewton-precond \(hy Preconditioned Truncated Newton
ld-tnewton-restart \(hy Truncated Newton with steepest-descent restarting
gn-direct \(hy Dividing Rectangles
auglag-eq \(hy Augmented Lagrangian algorithm with equality constraints only
ln-neldermead \(hy Nelder-Mead simplex algorithm
ln-cobyla \(hy Constrained Optimization BY Linear Approximation
gn-crs2-lm \(hy Controlled Random Search with Local Mutation
ld-var2 \(hy Shifted Limited-Memory Variable-Metric, Rank 2
ld-var1 \(hy Shifted Limited-Memory Variable-Metric, Rank 1
ld-mma \(hy Method of Moving Asymptotes
ld-lbfgs-nocedal \(hy None
g-mlsl \(hy Multi-Level Single-Linkage (require local optimization and bounds)
ld-lbfgs \(hy Low-storage BFGS
gn-direct-l \(hy Dividing Rectangles (locally biased)
ln-bobyqa \(hy Derivative-free Bound-constrained Optimization
ln-sbplx \(hy Subplex variant of Nelder-Mead
ln-newuoa-bound \(hy Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation
auglag \(hy Augmented Lagrangian algorithm
ln-praxis \(hy Gradient-free Local Optimization via the Principal-Axis Method
gn-direct-noscal \(hy Dividing Rectangles (unscaled)
ld-tnewton-precond-restart \(hy Preconditioned Truncated Newton with steepest-descent restarting
ld-slsqp \(hy Sequential Least-Squares Quadratic Programming
step = 0 (double)
Initial step size for gradient free methods. in [0, INF]
stop = -inf (double)
Stopping criterion: function value falls below this value. in [INF, INF]
xtola = 0 (double)
Stopping criterion: the absolute change of all x-values is below this value. in [0, INF]
xtolr = 0 (double)
Stopping criterion: the relative change of all x-values is below this value. in [0, INF]
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
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'.