Non-linear registration of 3d images.
mia-3dnonrigidreg-alt -o <out-transform> [options] <PLUGINS:3dimage/fullcost>
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)
output transformation For supported file types see PLUGINS:3dtransform/io
multi-resolution levels
Optimizer used for minimization For supported plugins see PLUGINS:minimizer/singlecost
transformation type For supported plugins see PLUGINS:3dimage/transform
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
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).
gauss
spacial Gauss filter kernel, supported parameters are:
w = 1 (int)
half filter width. in [0, 2147483647]
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]
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)
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., 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. 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 \(hy square of scaled difference
dot \(hy scalar product kernel
cross \(hy cross product kernel
ssd
3D image 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
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. in [-1.79769e+308, 1.79769e+308]
sthresh = 0 (double)
Threshold intensity value for source image. in [-1.79769e+308, 1.79769e+308]
bandpass
intensity bandpass filter, supported parameters are:
max = 3.40282e+38 (float)
maximum of the band. in [-3.40282e+38, 3.40282e+38]
min = 0 (float)
minimum of the band. in [-3.40282e+38, 3.40282e+38]
binarize
image binarize filter, supported parameters are:
max = 3.40282e+38 (float)
maximum of accepted range. in [0, 3.40282e+38]
min = 0 (float)
minimum of accepted range. in [0, 3.40282e+38]
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 =(input,required, 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. in [-3.40282e+38, 3.40282e+38]
b = 0 (float)
linear conversion parameter b. in [-3.40282e+38, 3.40282e+38]
map = opt (dict)
conversion mapping. Supported values are:
opt \(hy apply a linear transformation that maps the real input range to the full output range
range \(hy apply linear transformation that maps the input data type range to the output data type range
copy \(hy copy data when converting
linear \(hy apply linear transformation x -> a*x+b
optstat \(hy 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:
float \(hy floating point 32 bit
sbyte \(hy signed 8 bit
double \(hy floating point 64 bit
sint \(hy signed 32 bit
ushort \(hy unsigned 16 bit
sshort \(hy signed 16 bit
uint \(hy unsigned 32 bit
bit \(hy binary data
ubyte \(hy unsigned 8 bit
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)
blocksize in x direction. in [1, 2147483647]
by = 1 (uint)
blocksize in y direction. in [1, 2147483647]
bz = 1 (uint)
blocksize in z direction. in [1, 2147483647]
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)
filter width parameter. in [0, 2147483647]
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. in [-3.40282e+38, 3.40282e+38]
ref =(input,required, 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)
isometric target voxel size. in [0.001, 1e+06]
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)
number of classes. in [0, 255]
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
load
Load the input image from a file and use it to replace the current image in the pipeline., supported parameters are:
file =(input,required, 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 =(input,required, 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)
half filter width. in [1, 2147483647]
median
median 3d filter, supported parameters are:
w = 1 (int)
filter width parameter. in [0, 2147483647]
mlv
Mean of Least Variance 3D image filter, supported parameters are:
w = 1 (int)
filter width parameter. in [0, 2147483647]
msnormalizer
3D image mean-sigma normalizing filter, supported parameters are:
w = 1 (int)
half filter width. in [1, 2147483647]
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
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)
thresh value. in [0, 3.40282e+38]
w = 1 (int)
filter width parameter. in [0, 2147483647]
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)
target size in x direction (0:use input image size). in [0, 4294967295]
sy = 0 (uint)
target size in y direction (0:use input image size). in [0, 4294967295]
sz = 0 (uint)
target size in y direction (0:use input image size). in [0, 4294967295]
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
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 =(input,required, 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 =(output,required, 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 =(input,required, 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)
half filter width. in [1, 2147483647]
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)
Relative gradient norm threshold. The actual value threshhold value is thresh * (max_grad - min_grad) + min_grad. Bassins separated by gradients with a lower norm will be joined. in [0, 1]
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. 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 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. in [-1e+10, 1e+10]
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. in [-1e+10, 1e+10]
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, 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
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, 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:
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)
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)
sphere radius. in [0, 3.40282e+38]
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]] (streamable)
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]] (streamable)
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
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 debuging 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)
isotropic coefficient rate in pixels. in [1, 3.40282e+38]
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
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
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 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
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'.