mia-2dmyoserial-nonrigid(1)
NAME
mia-2dmyoserial-nonrigid - Run a serial registration of a series of 2D images.
SYNOPSIS
mia-2dmyoserial-nonrigid -i <in-file> -o <out-file> [options] <PLUGINS:2dimage/fullcost>
DESCRIPTION
mia-2dmyoserial-nonrigid This program runs the non-rigid motion compensation registration of an perfusion image series. The registration is run in a serial manner, this is, only images in temporal succession are registered, and the obtained transformations are applied accumulated to reach full registration. See e.g. Wollny, G., Ledesma-Carbayo, M.J., Kellman, P., Santos, A. "A New Similarity Measure for Non-Rigid Breathing Motion Compensation of Myocardial Perfusion MRI ". Proc 30th Annual International IEEE EMBS Conference, pp. 3389-3392. Vancouver, Aug. 2008, doi:10.1109/IEMBS.2008.4649933,
OPTIONS
File-IO
-i --in-file=(input, required); string
input perfusion data set
-o --out-file=(output, required); string
output perfusion data set
-R --registered=reg
file name base for registered fiels
Registration
-O --optimizer=gsl:opt=gd,step=0.1
Optimizer used for minimizationOptimizer used for
minimization For supported plugins see
PLUGINS:minimizer/singlecost
-l --mg-levels=3
multi-resolution levelsmulti-resolution levels
-f --transForm=spline:rate=16,penalty=[divcurl:weight=0.01]
transformation typetransformation type For supported
plugins see PLUGINS:2dimage/transform
-r --ref=-1
reference frame (-1 == use image in the middle)reference
frame (-1 == use image in the middle)
-k --skip=0
skip registration of these images at the beginning of the
seriesskip registration of these images at the beginning
of the series
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/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.
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
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.
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. 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
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)
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
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]]; 2dfvector
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]]; 2dfvector
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 in [1, inf)
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
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
PLUGINS: 2dtransform/splinepenalty
divcurl divcurl penalty on the transformation, supported parameters
are:
curl = 1; float in [0, inf)
penalty weight on curl.
div = 1; float in [0, inf)
penalty weight on divergence.
norm = 0; bool
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.
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..
max-step = 2; double in (0, inf)
Maximal absolute step size.
maxiter = 200; uint in [1, inf)
Stopping criterion: the maximum number of iterations.
min-step = 0.1; double in (0, inf)
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..
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..
gtola = 0; double in [0, inf)
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.
scale = 2; double in (1, inf)
Fallback fixed step size scaling.
step = 0.1; double in (0, inf)
Initial step size.
xtola = 0; double in [0, inf)
Stop if the inf-norm of x-update is below this value..
gsl optimizer plugin based on the multimin optimizers of the 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..
iter = 100; uint in [1, inf)
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
step = 0.001; double in (0, inf)
initial step size.
tol = 0.1; double in (0, inf)
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.
ftolr = 0; double in [0, inf)
Stopping criterion: the relative change of the
objective value is below this value.
higher = inf; double
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
lower = -inf; double
Lower boundary (equal for all parameters).
maxiter = 100; int in [1, inf)
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
step = 0; double in [0, inf)
Initial step size for gradient free methods.
stop = -inf; double
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.
xtolr = 0; double in [0, inf)
Stopping criterion: the relative change of all x-values
is below this value.
EXAMPLE
Register the perfusion series given in 'segment.set' to reference image
30. Skip two images at the beginning and using mutual information as
cost function, and penalize the transformation by divcurl with weight
5. Store the result in 'registered.set'.
mia-2dmyoserial-nonrigid -i segment.set -o registered.set -k 2 -r 30
image:cost=mi -f spline:rate=5,penalty=[divcurl:weight=5]
AUTHOR(s)
Gert Wollny
COPYRIGHT
This software is Copyright (c) 19992015 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'.
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