Run a registration of a series of 2d images.
mia-2dmyoica-nonrigid-parallel -i <in-file> -o <out-file> [options]
mia-2dmyoica-nonrigid-parallel This program implements the 2D version of the motion compensation algorithm described in Wollny G, Kellman P, Santos A, Ledesma-Carbayo M-J, "Automatic Motion Compensation of Free Breathing acquired Myocardial Perfusion Data by using Independent Component Analysis", Medical Image Analysis, 2012, DOI:10.1016/j.media.2012.02.004.This version of the program may run all registrations in parallel.
input perfusion data set
output perfusion data set
File name base for the registered images. Image type and numbering scheme are taken from the input images as given in the input data set.
save cropped set to this file, the image files will use the stem of the name as file name base
save segmentation feature images and initial ICA mixing matrix
for each registration pass save the reference images to files with the given name base
for each registration pass save intermediate registered images
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
ICA components 0 = automatic estimation
normalized ICs
don't strip the mean from the mixing curves
segment and scale the crop box around the LV (0=no segmentation)
skip images at the beginning of the series e.g. because as they are of other modalities
maximum number of iterations in ICA
Segmentation method
delta-peak \(hy difference of the peak enhancement images
features \(hy feature images
delta-feature \(hy difference of the feature images
minimal mean frequency a mixing curve can have to be considered to stem from brething. A healthy rest breating rate is 12 per minute. A negative value disables the test.
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).
Optimizer used for minimization. The string value will be used to construct a plug-in. For supported plugins see PLUGINS:minimizer/singlecost
start coefficinet rate in spines, gets divided by --c-rate-divider with every pass.
Cofficient rate divider for each pass.
Start divcurl weight, gets divided by --divcurl-divider with every pass.
Divcurl weight scaling with each new pass.
image cost, do not specify the src and ref parameters, these will be set by the program. The string value will be used to construct a plug-in. For supported plugins see PLUGINS:2dimage/fullcost
multi-resolution levels
registration passes
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)
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 perfusion series given in 'segment.set' by using automatic ICA estimation. Skip two images at the beginning and otherwiese use the default parameters. Store the result in 'registered.set'. mia-2dmyoica-nonrigid-parallel -i segment.set -o registered.set -k 2
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'.