Provided by: mia-tools_2.4.7-13_amd64 
NAME
mia-3dnonrigidreg-alt - Non-linear registration of 3D images.
SYNOPSIS
mia-3dnonrigidreg-alt -o <out-transform> [options] <PLUGINS:3dimage/fullcost>
DESCRIPTION
mia-3dnonrigidreg-alt This program runs a non-rigid registration based on the given cost
criteria and a given transformation model. Other than mia-3dnonrigidreg it doesn't support
specific command line parameters to provide the images. Instead the images are specified
dirctly when defining the cost function. Hence, image registrations can be executed that
optimize the aligmnet of more than one image pair at the same time. Note, however, that
all input images must be of the same dimension (in pixels)
OPTIONS
-o --out-transform=(required, output); io
output transformation
For supported file types see PLUGINS:3dtransform/io
-l --levels=3
multi-resolution levels
-O --optimizer=gsl:opt=gd,step=0.1
Optimizer used for minimization
For supported plugins see PLUGINS:minimizer/singlecost
-f --transForm=spline:rate=10
transformation type
For supported plugins see PLUGINS:3dimage/transform
Help & Info
-V --verbose=warning
verbosity of output, print messages of given level and higher priorities.
Supported priorities starting at lowest level are:
trace ‐ Function call trace
debug ‐ Debug output
info ‐ Low level messages
message ‐ Normal messages
warning ‐ Warnings
fail ‐ Report test failures
error ‐ Report errors
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).
PLUGINS: 1d/spacialkernel
cdiff Central difference filter kernel, mirror boundary conditions are used.
(no parameters)
gauss spacial Gauss filter kernel, supported parameters are:
w = 1; uint in [0, inf)
half filter width.
scharr This plugin provides the 1D folding kernel for the Scharr gradient filter
(no parameters)
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: 3dimage/combiner
absdiff Image combiner 'absdiff'
(no parameters)
add Image combiner 'add'
(no parameters)
div Image combiner 'div'
(no parameters)
mul Image combiner 'mul'
(no parameters)
sub Image combiner 'sub'
(no parameters)
PLUGINS: 3dimage/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.
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. Given normalized gradient fields $ _S$ of the src image and $ _R$ of the
ref image various evaluators are implemented., supported parameters are:
eval = ds; dict
plugin subtype (sq, ds,dot,cross). Supported values are:
ds ‐ square of scaled difference
dot ‐ scalar product kernel
cross ‐ cross product kernel
ssd 3D image 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
3D 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: 3dimage/filter
bandpass intensity bandpass filter, supported parameters are:
max = 3.40282e+38; float
maximum of the band.
min = 0; float
minimum of the band.
binarize image binarize filter, supported parameters are:
max = 3.40282e+38; float
maximum of accepted range.
min = 0; float
minimum of accepted range.
close morphological close, supported parameters are:
hint = black; string
a hint at the main image content (black|white).
shape = [sphere:r=2]; factory
structuring element. For supported plug-ins see PLUGINS:3dimage/shape
combiner Combine two images with the given combiner operator. if 'reverse' is set to
false, the first operator is the image passed through the filter pipeline, and
the second image is loaded from the file given with the 'image' parameter the
moment the filter is run., supported parameters are:
image =(required, input, io)
second image that is needed in the combiner. For supported file types see
PLUGINS:3dimage/io
op =(required, factory)
Image combiner to be applied to the images. For supported plug-ins see
PLUGINS:3dimage/combiner
reverse = 0; bool
reverse the order in which the images passed to the combiner.
convert image pixel format conversion filter, supported parameters are:
a = 1; float
linear conversion parameter a.
b = 0; float
linear conversion parameter b.
map = opt; dict
conversion mapping. Supported values are:
copy ‐ copy data when converting
linear ‐ apply linear transformation x -> a*x+b
range ‐ apply linear transformation that maps the input data type
range to the output data type range
opt ‐ apply a linear transformation that maps the real input range to
the full output range
optstat ‐ apply a linear transform that maps based on input mean and
variation to the full output range
repn = ubyte; dict
output pixel type. Supported values are:
bit ‐ binary data
sbyte ‐ signed 8 bit
ubyte ‐ unsigned 8 bit
sshort ‐ signed 16 bit
ushort ‐ unsigned 16 bit
sint ‐ signed 32 bit
uint ‐ unsigned 32 bit
slong ‐ signed 64 bit
ulong ‐ unsigned 64 bit
float ‐ floating point 32 bit
double ‐ floating point 64 bit
none ‐ no pixel type defined
crop Crop a region of an image, the region is always clamped to the original image
size in the sense that the given range is kept., supported parameters are:
end = [[4294967295,4294967295,4294967295]]; streamable
end of cropping range, maximum = (-1,-1,-1).
start = [[0,0,0]]; streamable
begin of cropping range.
dilate 3d image stack dilate filter, supported parameters are:
hint = black; string
a hint at the main image content (black|white).
shape = [sphere:r=2]; factory
structuring element. For supported plug-ins see PLUGINS:3dimage/shape
distance Evaluate the 3D distance transform of an image. If the image is a binary mask,
then result of the distance transform in each point corresponds to the Euclidian
distance to the mask. If the input image is of a scalar pixel value, then the
this scalar is interpreted as heighfield and the per pixel value adds to the
distance.
(no parameters)
downscale Downscale the input image by using a given block size to define the downscale
factor. Prior to scaling the image is filtered by a smoothing filter to
eliminate high frequency data and avoid aliasing artifacts., supported
parameters are:
b = [[1,1,1]]; 3dbounds
blocksize.
bx = 1; uint in [1, inf)
blocksize in x direction.
by = 1; uint in [1, inf)
blocksize in y direction.
bz = 1; uint in [1, inf)
blocksize in z direction.
kernel = gauss; factory
smoothing filter kernel to be applied, the size of the filter is estimated
based on the blocksize.. For supported plug-ins see
PLUGINS:1d/spacialkernel
erode 3d image stack erode filter, supported parameters are:
hint = black; string
a hint at the main image content (black|white).
shape = [sphere:r=2]; factory
structuring element. For supported plug-ins see PLUGINS:3dimage/shape
gauss isotropic 3D gauss filter, supported parameters are:
w = 1; int in [0, inf)
filter width parameter.
gradnorm 3D image to gradient norm filter
(no parameters)
growmask Use an input binary mask and a reference gray scale image to do region growing
by adding the neighborhood pixels of an already added pixel if the have a lower
intensity that is above the given threshold., supported parameters are:
min = 1; float
lower threshold for mask growing.
ref =(required, input, io)
reference image for mask region growing. For supported file types see
PLUGINS:3dimage/io
shape = 6n; factory
neighborhood mask. For supported plug-ins see PLUGINS:3dimage/shape
invert intensity invert filter
(no parameters)
isovoxel This filter scales an image to make the voxel size isometric and its size to
correspond to the given value, supported parameters are:
interp = [bspline:d=3]; factory
interpolation kernel to be used . For supported plug-ins see
PLUGINS:1d/splinekernel
size = 1; float in (0, inf)
isometric target voxel size.
kmeans 3D image k-means filter. In the output image the pixel value represents the
class membership and the class centers are stored as attribute in the image.,
supported parameters are:
c = 3; int in [2, inf)
number of classes.
label A filter to label the connected components of a binary image., supported
parameters are:
n = 6n; factory
neighborhood mask. For supported plug-ins see PLUGINS:3dimage/shape
labelmap Image filter to remap label id's. Only applicable to images with integer valued
intensities/labels., supported parameters are:
map =(required, input, string)
Label mapping file.
labelscale
A filter that only creates output voxels that are already created in the input
image. Scaling is done by using a voting algorithms that selects the target
pixel value based on the highest pixel count of a certain label in the
corresponding source region. If the region comprises two labels with the same
count, the one with the lower number wins., supported parameters are:
out-size =(required, 3dbounds)
target size given as two coma separated values.
load Load the input image from a file and use it to replace the current image in the
pipeline., supported parameters are:
file =(required, input, io)
name of the input file to load from.. For supported file types see
PLUGINS:3dimage/io
lvdownscale
This is a label voting downscale filter. It adownscales a 3D image by blocks.
For each block the (non-zero) label that appears most times in the block is
issued as output pixel in the target image. If two labels appear the same number
of times, the one with the lower absolute value wins., supported parameters are:
b = [[1,1,1]]; 3dbounds
blocksize for the downscaling. Each block will be represented by one pixel
in the target image..
mask Mask an image, one image is taken from the parameters list and the other from
the normal filter input. Both images must be of the same dimensions and one must
be binary. The attributes of the image coming through the filter pipeline are
preserved. The output pixel type corresponds to the input image that is not
binary., supported parameters are:
input =(required, input, io)
second input image file name. For supported file types see
PLUGINS:3dimage/io
mean 3D image mean filter, supported parameters are:
w = 1; int in [1, inf)
half filter width.
median median 3d filter, supported parameters are:
w = 1; int in [1, inf)
filter width parameter.
mlv Mean of Least Variance 3D image filter, supported parameters are:
w = 1; int in [1, inf)
filter width parameter.
msnormalizer
3D image mean-sigma normalizing filter, supported parameters are:
w = 1; int in [1, inf)
half filter width.
open morphological open, supported parameters are:
hint = black; string
a hint at the main image content (black|white).
shape = [sphere:r=2]; factory
structuring element. For supported plug-ins see PLUGINS:3dimage/shape
reorient 3D image reorientation filter, supported parameters are:
map = xyz; dict
oriantation mapping to be applied. Supported values are:
xyz ‐ keep orientation
p-yzx ‐ permutate x->z->y->x
p-zxy ‐ permutate x->y->z->x
f-yz ‐ flip y-z
f-xy ‐ flip x-y
f-xz ‐ flip x-z
r-x90 ‐ rotate around x-axis clockwise 90 degree
r-x180 ‐ rotate around x-axis clockwise 180 degree
r-x270 ‐ rotate around x-axis clockwise 270 degree
r-y90 ‐ rotate around y-axis clockwise 90 degree
r-y180 ‐ rotate around y-axis clockwise 180 degree
r-y270 ‐ rotate around y-axis clockwise 270 degree
r-z90 ‐ rotate around z-axis clockwise 90 degree
r-z180 ‐ rotate around z-axis clockwise 180 degree
r-z270 ‐ rotate around z-axis clockwise 270 degree
resize Resize an image. The original data is centered within the new sized image.,
supported parameters are:
size = [[0,0,0]]; streamable
new size of the image a size 0 indicates to keep the size for the
corresponding dimension..
sandp salt and pepper 3d filter, supported parameters are:
thresh = 100; float in [0, inf)
thresh value.
w = 1; int in [1, inf)
filter width parameter.
scale 3D image filter that scales to a given target size , supported parameters are:
interp = [bspline:d=3]; factory
interpolation kernel to be used . For supported plug-ins see
PLUGINS:1d/splinekernel
s = [[0,0,0]]; 3dbounds
target size to set all components at once (component 0:use input image
size).
sx = 0; uint in [0, inf)
target size in x direction (0:use input image size).
sy = 0; uint in [0, inf)
target size in y direction (0:use input image size).
sz = 0; uint in [0, inf)
target size in y direction (0:use input image size).
scharr The 3D Scharr filter for gradient evaluation. Note that the output pixel type of
the filtered image is the same as the input pixel type, so converting the input
beforehand to a floating point valued image is recommendable., supported
parameters are:
dir = x; dict
Gradient direction. Supported values are:
x ‐ gradient in x-direction
y ‐ gradient in y-direction
z ‐ gradient in z-direction
selectbig A filter that creats a binary mask representing the intensity with the highest
pixel count.The pixel value 0 will be ignored, and if two intensities have the
same pixel count, then the result is undefined. The input pixel must have an
integral pixel type.
(no parameters)
sepconv 3D image intensity separaple convolution filter, supported parameters are:
kx = [gauss:w=1]; factory
filter kernel in x-direction. For supported plug-ins see
PLUGINS:1d/spacialkernel
ky = [gauss:w=1]; factory
filter kernel in y-direction. For supported plug-ins see
PLUGINS:1d/spacialkernel
kz = [gauss:w=1]; factory
filter kernel in z-direction. For supported plug-ins see
PLUGINS:1d/spacialkernel
sobel The 2D Sobel filter for gradient evaluation. Note that the output pixel type of
the filtered image is the same as the input pixel type, so converting the input
beforehand to a floating point valued image is recommendable., supported
parameters are:
dir = x; dict
Gradient direction. Supported values are:
x ‐ gradient in x-direction
y ‐ gradient in y-direction
z ‐ gradient in z-direction
sws seeded watershead. The algorithm extracts exactly so many reagions as initial
labels are given in the seed image., supported parameters are:
grad = 0; bool
Interpret the input image as gradient. .
mark = 0; bool
Mark the segmented watersheds with a special gray scale value.
n = [sphere:r=1]; factory
Neighborhood for watershead region growing. For supported plug-ins see
PLUGINS:3dimage/shape
seed =(required, input, string)
seed input image containing the lables for the initial regions.
tee Save the input image to a file and also pass it through to the next filter,
supported parameters are:
file =(required, output, io)
name of the output file to save the image too.. For supported file types
see PLUGINS:3dimage/io
thinning 3D morphological thinning, based on: Lee and Kashyap, 'Building Skeleton Models
via 3-D Medial Surface/Axis Thinning Algorithms', Graphical Models and Image
Processing, 56(6):462-478, 1994. This implementation only supports the 26
neighbourhood.
(no parameters)
transform Transform the input image with the given transformation., supported parameters
are:
file =(required, input, io)
Name of the file containing the transformation.. For supported file types
see PLUGINS:3dtransform/io
imgboundary = ; factory
override image interpolation boundary conditions. For supported plug-ins
see PLUGINS:1d/splinebc
imgkernel = ; factory
override image interpolator kernel. For supported plug-ins see
PLUGINS:1d/splinekernel
variance 3D image variance filter, supported parameters are:
w = 1; int in [1, inf)
half filter width.
ws basic watershead segmentation., supported parameters are:
evalgrad = 0; bool
Set to 1 if the input image does not represent a gradient norm image.
mark = 0; bool
Mark the segmented watersheds with a special gray scale value.
n = [sphere:r=1]; factory
Neighborhood for watershead region growing. For supported plug-ins see
PLUGINS:3dimage/shape
thresh = 0; float in [0, 1)
Relative gradient norm threshold. The actual value threshold value is
thresh * (max_grad - min_grad) + min_grad. Bassins separated by gradients
with a lower norm will be joined.
PLUGINS: 3dimage/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:3dimage/cost
debug = 0; bool
Save intermediate resuts for debugging.
ref =(input, io)
Reference image. For supported file types see PLUGINS:3dimage/io
src =(input, io)
Study image. For supported file types see PLUGINS:3dimage/io
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:
maxlabel = 256; int in [2, 32000]
maximum number of labels to consider.
ref =(input, io)
Reference image. For supported file types see PLUGINS:3dimage/io
src =(input, io)
Study image. For supported file types see PLUGINS:3dimage/io
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 mask may be pre-filtered - after pre-filtering
the masks must be of bit-type.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:3dimage/maskedcost
ref =(input, io)
Reference image. For supported file types see PLUGINS:3dimage/io
ref-mask =(input, io)
Reference image mask (binary). For supported file types see
PLUGINS:3dimage/io
ref-mask-filter = ; factory
Filter to prepare the reference mask image, the output must be a binary
image.. For supported plug-ins see PLUGINS:3dimage/filter
src =(input, io)
Study image. For supported file types see PLUGINS:3dimage/io
src-mask =(input, io)
Study image mask (binary). For supported file types see
PLUGINS:3dimage/io
src-mask-filter = ; factory
Filter to prepare the study mask image, the output must be a binary
image.. For supported plug-ins see PLUGINS:3dimage/filter
weight = 1; float
weight of cost function.
taggedssd Evaluates the Sum of Squared Differences similarity measure by using three
tagged image pairs. The cost function value is evaluated based on all image
pairs, but the gradient is composed by composing its component based on the tag
direction., supported parameters are:
refx =(input, io)
Reference image X-tag. For supported file types see PLUGINS:3dimage/io
refy =(input, io)
Reference image Y-tag. For supported file types see PLUGINS:3dimage/io
refz =(input, io)
Reference image Z-tag. For supported file types see PLUGINS:3dimage/io
srcx =(input, io)
Study image X-tag. For supported file types see PLUGINS:3dimage/io
srcy =(input, io)
Study image Y-tag. For supported file types see PLUGINS:3dimage/io
srcz =(input, io)
Study image Z-tag. For supported file types see PLUGINS:3dimage/io
weight = 1; float
weight of cost function.
PLUGINS: 3dimage/io
analyze Analyze 7.5 image
Recognized file extensions: .HDR, .hdr
Supported element types:
unsigned 8 bit, signed 16 bit, signed 32 bit, floating point 32 bit,
floating point 64 bit
datapool Virtual IO to and from the internal data pool
Recognized file extensions: .@
dicom Dicom image series as 3D
Recognized file extensions: .DCM, .dcm
Supported element types:
signed 16 bit, unsigned 16 bit
hdf5 HDF5 3D image IO
Recognized file extensions: .H5, .h5
Supported element types:
binary data, signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit,
signed 32 bit, unsigned 32 bit, signed 64 bit, unsigned 64 bit, floating
point 32 bit, floating point 64 bit
inria INRIA image
Recognized file extensions: .INR, .inr
Supported element types:
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
mhd MetaIO 3D image IO using the VTK implementation (experimental).
Recognized file extensions: .MHA, .MHD, .mha, .mhd
Supported element types:
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
nifti NIFTI-1 3D image IO. The orientation is transformed in the same way like it is
done with 'dicomtonifti --no-reorder' from the vtk-dicom package.
Recognized file extensions: .NII, .nii
Supported element types:
signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit, signed 32
bit, unsigned 32 bit, signed 64 bit, unsigned 64 bit, floating point 32
bit, floating point 64 bit
vff VFF Sun raster format
Recognized file extensions: .VFF, .vff
Supported element types:
unsigned 8 bit, signed 16 bit
vista Vista 3D
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
vti 3D image VTK-XML in- and output (experimental).
Recognized file extensions: .VTI, .vti
Supported element types:
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
vtk 3D VTK image legacy in- and output (experimental).
Recognized file extensions: .VTK, .VTKIMAGE, .vtk, .vtkimage
Supported element types:
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: 3dimage/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: 3dimage/shape
18n 18n neighborhood 3D shape creator
(no parameters)
26n 26n neighborhood 3D shape creator
(no parameters)
6n 6n neighborhood 3D shape creator
(no parameters)
sphere Closed spherical shape neighborhood including the pixels within a given radius
r., supported parameters are:
r = 2; float in (0, inf)
sphere radius.
PLUGINS: 3dimage/transform
affine Affine transformation (12 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
axisrot Restricted rotation transformation (1 degrees of freedom). The transformation is
restricted to the rotation around the given axis about the given rotation
center, supported parameters are:
axis =(required, 3dfvector)
rotation axis.
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
origin =(required, 3dfvector)
center of the transformation.
raffine Restricted affine transformation (3 degrees of freedom). The transformation is
restricted to the rotation around the given axis and shearing along the two axis
perpendicular to the given one, supported parameters are:
axis =(required, 3dfvector)
rotation axis.
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
origin =(required, 3dfvector)
center of the transformation.
rigid Rigid transformation, i.e. rotation and translation (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
origin = [[0,0,0]]; 3dfvector
Relative rotation center, i.e. <0.5,0.5,0.5> corresponds to the center of
the volume.
rotation Rotation transformation (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
origin = [[0,0,0]]; 3dfvector
Relative rotation center, i.e. <0.5,0.5,0.5> corresponds to the center of
the volume.
rotbend Restricted transformation (4 degrees of freedom). The transformation is
restricted to the rotation around the x and y axis and a bending along the x
axis, independedn in each direction, with the bending increasing with the
squared distance from the rotation axis., 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
norot = 0; bool
Don't optimize the rotation.
origin =(required, 3dfvector)
center of the transformation.
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 = [[0,0,0]]; 3dfvector
anisotropic coefficient rate in pixels, nonpositive values will be
overwritten by the 'rate' value..
debug = 0; bool
enable additional debugging output.
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 energy term. For supported plug-ins see
PLUGINS:3dtransform/splinepenalty
rate = 10; float in [1, inf)
isotropic coefficient rate in pixels.
translate 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
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: 3dtransform/io
bbs Binary (non-portable) serialized IO of 3D transformations
Recognized file extensions: .bbs
datapool Virtual IO to and from the internal data pool
Recognized file extensions: .@
vista Vista storage of 3D transformations
Recognized file extensions: .v, .v3dt
xml XML serialized IO of 3D transformations
Recognized file extensions: .x3dt
PLUGINS: 3dtransform/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:
simplex ‐ Simplex algorithm of Nelder and Mead
cg-fr ‐ Flecher-Reeves conjugate gradient algorithm
cg-pr ‐ Polak-Ribiere conjugate gradient algorithm
bfgs ‐ Broyden-Fletcher-Goldfarb-Shann
bfgs2 ‐ Broyden-Fletcher-Goldfarb-Shann (most efficient version)
gd ‐ Gradient descent.
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-direct ‐ Dividing Rectangles
gn-direct-l ‐ Dividing Rectangles (locally biased)
gn-direct-l-rand ‐ Dividing Rectangles (locally biased, randomized)
gn-direct-noscal ‐ Dividing Rectangles (unscaled)
gn-direct-l-noscal ‐ Dividing Rectangles (unscaled, locally biased)
gn-direct-l-rand-noscale ‐ Dividing Rectangles (unscaled, locally
biased, randomized)
gn-orig-direct ‐ Dividing Rectangles (original implementation)
gn-orig-direct-l ‐ Dividing Rectangles (original implementation,
locally biased)
ld-lbfgs-nocedal ‐ None
ld-lbfgs ‐ Low-storage BFGS
ln-praxis ‐ Gradient-free Local Optimization via the Principal-Axis
Method
ld-var1 ‐ Shifted Limited-Memory Variable-Metric, Rank 1
ld-var2 ‐ Shifted Limited-Memory Variable-Metric, Rank 2
ld-tnewton ‐ Truncated Newton
ld-tnewton-restart ‐ Truncated Newton with steepest-descent restarting
ld-tnewton-precond ‐ Preconditioned Truncated Newton
ld-tnewton-precond-restart ‐ Preconditioned Truncated Newton with
steepest-descent restarting
gn-crs2-lm ‐ Controlled Random Search with Local Mutation
ld-mma ‐ Method of Moving Asymptotes
ln-cobyla ‐ Constrained Optimization BY Linear Approximation
ln-newuoa ‐ Derivative-free Unconstrained Optimization by Iteratively
Constructed Quadratic Approximation
ln-newuoa-bound ‐ Derivative-free Bound-constrained Optimization by
Iteratively Constructed Quadratic Approximation
ln-neldermead ‐ Nelder-Mead simplex algorithm
ln-sbplx ‐ Subplex variant of Nelder-Mead
ln-bobyqa ‐ Derivative-free Bound-constrained Optimization
gn-isres ‐ Improved Stochastic Ranking Evolution Strategy
none ‐ don't specify algorithm
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-direct ‐ Dividing Rectangles
gn-direct-l ‐ Dividing Rectangles (locally biased)
gn-direct-l-rand ‐ Dividing Rectangles (locally biased, randomized)
gn-direct-noscal ‐ Dividing Rectangles (unscaled)
gn-direct-l-noscal ‐ Dividing Rectangles (unscaled, locally biased)
gn-direct-l-rand-noscale ‐ Dividing Rectangles (unscaled, locally
biased, randomized)
gn-orig-direct ‐ Dividing Rectangles (original implementation)
gn-orig-direct-l ‐ Dividing Rectangles (original implementation,
locally biased)
ld-lbfgs-nocedal ‐ None
ld-lbfgs ‐ Low-storage BFGS
ln-praxis ‐ Gradient-free Local Optimization via the Principal-Axis
Method
ld-var1 ‐ Shifted Limited-Memory Variable-Metric, Rank 1
ld-var2 ‐ Shifted Limited-Memory Variable-Metric, Rank 2
ld-tnewton ‐ Truncated Newton
ld-tnewton-restart ‐ Truncated Newton with steepest-descent restarting
ld-tnewton-precond ‐ Preconditioned Truncated Newton
ld-tnewton-precond-restart ‐ Preconditioned Truncated Newton with
steepest-descent restarting
gn-crs2-lm ‐ Controlled Random Search with Local Mutation
ld-mma ‐ Method of Moving Asymptotes
ln-cobyla ‐ Constrained Optimization BY Linear Approximation
ln-newuoa ‐ Derivative-free Unconstrained Optimization by Iteratively
Constructed Quadratic Approximation
ln-newuoa-bound ‐ Derivative-free Bound-constrained Optimization by
Iteratively Constructed Quadratic Approximation
ln-neldermead ‐ Nelder-Mead simplex algorithm
ln-sbplx ‐ Subplex variant of Nelder-Mead
ln-bobyqa ‐ Derivative-free Bound-constrained Optimization
gn-isres ‐ Improved Stochastic Ranking Evolution Strategy
auglag ‐ Augmented Lagrangian algorithm
auglag-eq ‐ Augmented Lagrangian algorithm with equality constraints
only
g-mlsl ‐ Multi-Level Single-Linkage (require local optimization and
bounds)
g-mlsl-lds ‐ Multi-Level Single-Linkage (low-discrepancy-sequence,
require local gradient based optimization and bounds)
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 image test.v to image ref.v by using a spline transformation with a coefficient
rate of 5 and write the registered image to reg.v. Use two multiresolution levels, ssd as
image cost function and divcurl weighted by 10.0 as transformation smoothness penalty. The
resulting transformation is saved in reg.vf.
mia-3dnonrigidreg-alt -o reg.vf -l 2 -f spline:rate=3 image:cost=ssd,src=test.v,ref=ref.v
divcurl:weight=10
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'.