This is the command mia-3dprealign-nonrigid that can be run in the OnWorks free hosting provider using one of our multiple free online workstations such as Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator

**PROGRAM:**

**NAME**

mia-3dprealign-nonrigid - Registration of a series of 3D images.

**SYNOPSIS**

**mia-3dprealign-nonrigid**

**-i**

**<in-file>**

**-o**

**<out-file>**

**[options]**

**DESCRIPTION**

**mia-3dprealign-nonrigid**This program runs the non-rigid registration of an image series by

first registering an already aligned subset of the images to one reference, and then by

registering the remaining images by using synthetic references. The is a 3D version of G.

Wollny, M-J Ledesma-Cabryo, P.Kellman, and A.Santos, "Exploiting Quasiperiodicity in

Motion Correction of Free-Breathing," IEEE Transactions on Medical Imaging, 29(8), 2010.

**OPTIONS**

**File-IO**

-i --in-file=(input, required); io

input images following the naming pattern nameXXXX.ext For supported file

types see PLUGINS:3dimage/io

-o --out-file=(output, required); io

file name base for registered files given as C-format string For supported

file types see PLUGINS:3dimage/io

--save-references

Save synthetic references to files refXXXX.v

**Preconditions**

**&**

**Preprocessing**

-k --skip=0

Skip images at the begin of the seriesSkip images at the begin of the series

--preskip=20

Skip images at the beginning+skip of the series when searching for high

contrats imageSkip images at the beginning+skip of the series when searching

for high contrats image

--postskip=2

Skip images at the end of the series when searching for high contrats

imageSkip images at the end of the series when searching for high contrats

image

--max-candidates=20

maximum number of candidates for global reference imagemaximum number of

candidates for global reference image

-S --cost-series=image:cost=[ngf:eval=ds]

Const function to use for the analysis of the seriesConst function to use

for the analysis of the series For supported plugins see

PLUGINS:3dimage/fullcost

--ref-idx=

save reference index number to this file

-R --global-reference=-1

save reference index number to this filesave reference index number to this

file

-D --max-subset-delta=0

Maximum delta between two elements of the prealigned subsetMaximum delta

between two elements of the prealigned subset

**Registration**

-O --optimizer=gsl:opt=gd,step=0.01

Optimizer used for minimizationOptimizer used for minimization For

supported plugins see PLUGINS:minimizer/singlecost

-l --mr-levels=3

multi-resolution levelsmulti-resolution levels

-f --transForm=spline

transformation typetransformation type For supported plugins see

PLUGINS:3dimage/transform

-1 --cost-subset=image:cost=[ngf:eval=ds]

Cost function for registration during the subset registrationCost function

for registration during the subset registration For supported plugins see

PLUGINS:3dimage/fullcost

-2 --cost-final=image:cost=ssd

Cost function for registration during the final registrationCost function

for registration during the final registration For supported plugins see

PLUGINS:3dimage/fullcost

**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/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.

**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__=(input, required, string)

second image that is needed in the combiner.

__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:

__opt__‐ apply a linear transformation that maps the real input range to

the full output range

__range__‐ apply linear transformation that maps the input data type

range to the output data type range

__copy__‐ copy data when converting

__linear__‐ apply linear transformation x -> a*x+b

__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:

__none__‐ no pixel type defined

__float__‐ floating point 32 bit

__sbyte__‐ signed 8 bit

__ulong__‐ unsigned 64 bit

__double__‐ floating point 64 bit

__sint__‐ signed 32 bit

__ushort__‐ unsigned 16 bit

__sshort__‐ signed 16 bit

__uint__‐ unsigned 32 bit

__slong__‐ signed 64 bit

__bit__‐ binary data

__ubyte__‐ 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 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; string

smoothing filter kernel to be applied, the size of the filter is estimated

based on the blocksize..

**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__=(input, required, string)

reference image for mask region growing.

__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__=(input, required, 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__=(input, required, string)

name of the input file to load from..

**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, string)

second input image file name.

**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:

__p-zxy__‐ permutate x->y->z->x

__r-x180__‐ rotate around x-axis clockwise 180 degree

__xyz__‐ keep orientation

__p-yzx__‐ permutate x->z->y->x

__r-z180__‐ rotate around z-axis clockwise 180 degree

__r-y270__‐ rotate around y-axis clockwise 270 degree

__f-xz__‐ flip x-z

__f-yz__‐ flip y-z

__r-x90__‐ rotate around x-axis clockwise 90 degree

__r-y90__‐ rotate around y-axis clockwise 90 degree

__r-x270__‐ rotate around x-axis clockwise 270 degree

__r-z270__‐ rotate around z-axis clockwise 270 degree

__r-z90__‐ rotate around z-axis clockwise 90 degree

__f-xy__‐ flip x-y

__r-y180__‐ rotate around y-axis clockwise 180 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).

**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, string)

name of the output file to save the image too..

**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, string)

Name of the file containing the transformation..

__imgboundary__= ; string

override image interpolation boundary conditions.

__imgkernel__= ; string

override image interpolator kernel.

**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, 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:

__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 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, string)

Reference image.

__ref-mask__=(input, string)

Reference image mask (binary).

__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, string)

Study image.

__src-mask__=(input, string)

Study image mask (binary).

__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, string)

Reference image X-tag.

__refy__=(input, string)

Reference image Y-tag.

__refz__=(input, string)

Reference image Z-tag.

__srcx__=(input, string)

Study image X-tag.

__srcy__=(input, string)

Study image Y-tag.

__srcz__=(input, string)

Study image Z-tag.

__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

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:

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:** **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 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 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 ofthe 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 image series given by images imageXXXX.v by optimizing a spline based

transformation with a coefficient rate of 16 pixel, skipping two images at the beginning

and using normalized gradient fields as initial cost measure and SSD as final measure.

Penalize the transformation by using divcurl with aweight of 2.0. As optimizer an nlopt

based newton method is used.

mia-3dprealign-nonrigid mia-3dprealign-nonrigid -i imageXXXX.v -o registered -t vista -k

2-F spline:rate=16,penalty=[divcurl:weight=2] -1 image:cost=[ngf:eval=ds] -2

image:cost=ssd -O nlopt:opt=ld-var1,xtola=0.001,ftolr=0.001,maxiter=300

**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'.

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