mia-2dimageregistration - Run a 2d image registration.
mia-2dimageregistration -i <in-image> -r <ref-image> -t <transformation> [options] <PLUGINS:2dimage/fullcost>
mia-2dimageregistration This program runs registration of two images optimizing a transformation of the given transformation model by optimizing certain cost measures that are given as free parameters.
File-IO -i --in-image=(input, required); io test image to be registered For supported file types see PLUGINS:2dimage/io -r --ref-image=(input, required); io reference image to be registered to For supported file types see PLUGINS:2dimage/io -o --out-image=(output); io registered output image For supported file types see PLUGINS:2dimage/io -t --transformation=(output, required); io output transformation comprising the registration For supported file types see PLUGINS:2dtransform/io 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 Parameters -l --levels=3 multi-resolution levelsmulti-resolution levels -O --optimizer=gsl:opt=gd,step=0.1 Optimizer used for minimizationOptimizer used for minimization For supported plugins see PLUGINS:minimizer/singlecost -R --refiner= optimizer used for refinement after the main optimizer was calledoptimizer used for refinement after the main optimizer was called For supported plugins see PLUGINS:minimizer/singlecost -f --transForm=spline transformation typetransformation type For supported plugins see PLUGINS:2dimage/transform 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).
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 in [0, 5] Spline degree. omoms OMoms-spline kernel creation, supported parameters are: d = 3; int in [3, 3] Spline degree.
lncc local normalized cross correlation with masking support., supported parameters are: w = 5; uint in [1, 256] half width of the window used for evaluating the localized cross correlation. lsd Least-Squares Distance measure (no parameters) mi Spline parzen based mutual information., supported parameters are: cut = 0; float in [0, 40] Percentage of pixels to cut at high and low intensities to remove outliers. mbins = 64; uint in [1, 256] Number of histogram bins used for the moving image. mkernel = [bspline:d=3]; factory Spline kernel for moving image parzen hinstogram. For supported plug-ins see PLUGINS:1d/splinekernel rbins = 64; uint in [1, 256] Number of histogram bins used for the reference image. rkernel = [bspline:d=0]; factory Spline kernel for reference image parzen hinstogram. For supported plug-ins see PLUGINS:1d/splinekernel ncc normalized cross correlation. (no parameters) ngf This function evaluates the image similarity based on normalized gradient fields. Various evaluation kernels are available., supported parameters are: eval = ds; dict plugin subtype. Supported values are: sq square of difference ds square of scaled difference dot scalar product kernel cross cross product kernel ssd 2D imaga cost: sum of squared differences, supported parameters are: autothresh = 0; float in [0, 1000] Use automatic masking of the moving image by only takeing intensity values into accound that are larger than the given threshold. norm = 0; bool Set whether the metric should be normalized by the number of image pixels. ssd-automask 2D image cost: sum of squared differences, with automasking based on given thresholds, supported parameters are: rthresh = 0; double Threshold intensity value for reference image. sthresh = 0; double Threshold intensity value for source image.
image Generalized image similarity cost function that also handles multi-resolution processing. The actual similarity measure is given es extra parameter., supported parameters are: cost = ssd; factory Cost function kernel. For supported plug-ins see PLUGINS:2dimage/cost debug = 0; bool Save intermediate resuts for debugging. ref =(input, string) Reference image. src =(input, string) Study image. weight = 1; float weight of cost function. labelimage Similarity cost function that maps labels of two images and handles label-preserving multi-resolution processing., supported parameters are: debug = 0; int in [0, 1] write the distance transforms to a 3D image. maxlabel = 256; int in [2, 32000] maximum number of labels to consider. ref =(input, string) Reference image. src =(input, string) Study image. weight = 1; float weight of cost function. maskedimage Generalized masked image similarity cost function that also handles multi-resolution processing. The provided masks should be densly filled regions in multi-resolution procesing because otherwise the mask information may get lost when downscaling the image. The reference mask and the transformed mask of the study image are combined by binary AND. The actual similarity measure is given es extra parameter., supported parameters are: cost = ssd; factory Cost function kernel. For supported plug-ins see PLUGINS:2dimage/maskedcost ref =(input, string) Reference image. ref-mask =(input, string) Reference image mask (binary). src =(input, string) Study image. src-mask =(input, string) Study image mask (binary). weight = 1; float weight of cost function.
bmp BMP 2D-image input/output support. The plug-in supports reading and writing of binary images and 8-bit gray scale images. read-only support is provided for 4-bit gray scale images. The color table is ignored and the pixel values are taken as literal gray scale values. Recognized file extensions: .BMP, .bmp Supported element types: binary data, unsigned 8 bit datapool Virtual IO to and from the internal data pool Recognized file extensions: .@ dicom 2D image io for DICOM Recognized file extensions: .DCM, .dcm Supported element types: signed 16 bit, unsigned 16 bit exr a 2dimage io plugin for OpenEXR images Recognized file extensions: .EXR, .exr Supported element types: unsigned 32 bit, floating point 32 bit jpg a 2dimage io plugin for jpeg gray scale images Recognized file extensions: .JPEG, .JPG, .jpeg, .jpg Supported element types: unsigned 8 bit png a 2dimage io plugin for png images Recognized file extensions: .PNG, .png Supported element types: binary data, unsigned 8 bit, unsigned 16 bit raw RAW 2D-image output support Recognized file extensions: .RAW, .raw Supported element types: binary data, signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit, signed 32 bit, unsigned 32 bit, floating point 32 bit, floating point 64 bit tif TIFF 2D-image input/output support Recognized file extensions: .TIF, .TIFF, .tif, .tiff Supported element types: binary data, unsigned 8 bit, unsigned 16 bit, unsigned 32 bit vista a 2dimage io plugin for vista images Recognized file extensions: .-, .V, .VISTA, .v, .vista Supported element types: binary data, signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit, signed 32 bit, unsigned 32 bit, floating point 32 bit, floating point 64 bit
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)
affine Affine transformation (six degrees of freedom)., supported parameters are: imgboundary = mirror; factory image interpolation boundary conditions. For supported plug-ins see PLUGINS:1d/splinebc imgkernel = [bspline:d=3]; factory image interpolator kernel. For supported plug-ins see PLUGINS:1d/splinekernel rigid Rigid transformations (i.e. rotation and translation, three degrees of freedom)., supported parameters are: imgboundary = mirror; factory image interpolation boundary conditions. For supported plug-ins see PLUGINS:1d/splinebc imgkernel = [bspline:d=3]; factory image interpolator kernel. For supported plug-ins see PLUGINS:1d/splinekernel rot-center = [[0,0]]; 2dfvector Relative rotation center, i.e. <0.5,0.5> corresponds to the center of the support rectangle. rotation Rotation transformations (i.e. rotation about a given center, one degree of freedom)., supported parameters are: imgboundary = mirror; factory image interpolation boundary conditions. For supported plug-ins see PLUGINS:1d/splinebc imgkernel = [bspline:d=3]; factory image interpolator kernel. For supported plug-ins see PLUGINS:1d/splinekernel rot-center = [[0,0]]; 2dfvector Relative rotation center, i.e. <0.5,0.5> corresponds to the center of the support rectangle. spline Free-form transformation that can be described by a set of B- spline coefficients and an underlying B-spline kernel., supported parameters are: anisorate = [[0,0]]; 2dfvector anisotropic coefficient rate in pixels, nonpositive values will be overwritten by the 'rate' value.. imgboundary = mirror; factory image interpolation boundary conditions. For supported plug-ins see PLUGINS:1d/splinebc imgkernel = [bspline:d=3]; factory image interpolator kernel. For supported plug-ins see PLUGINS:1d/splinekernel kernel = [bspline:d=3]; factory transformation spline kernel.. For supported plug-ins see PLUGINS:1d/splinekernel penalty = ; factory Transformation penalty term. For supported plug-ins see PLUGINS:2dtransform/splinepenalty rate = 10; float in [1, inf) isotropic coefficient rate in pixels. translate Translation only (two degrees of freedom), supported parameters are: imgboundary = mirror; factory image interpolation boundary conditions. For supported plug-ins see PLUGINS:1d/splinebc imgkernel = [bspline:d=3]; factory image interpolator kernel. For supported plug-ins see PLUGINS:1d/splinekernel vf This plug-in implements a transformation that defines a translation for each point of the grid defining the domain of the transformation., supported parameters are: imgboundary = mirror; factory image interpolation boundary conditions. For supported plug-ins see PLUGINS:1d/splinebc imgkernel = [bspline:d=3]; factory image interpolator kernel. For supported plug-ins see PLUGINS:1d/splinekernel
bbs Binary (non-portable) serialized IO of 2D transformations Recognized file extensions: .bbs datapool Virtual IO to and from the internal data pool Recognized file extensions: .@ vista Vista storage of 2D transformations Recognized file extensions: .v2dt xml XML serialized IO of 2D transformations Recognized file extensions: .x2dt
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.
gdas Gradient descent with automatic step size correction., supported parameters are: ftolr = 0; double in [0, inf) Stop if the relative change of the criterion is below.. max-step = 2; double in (0, inf) Maximal absolute step size. maxiter = 200; uint in [1, inf) Stopping criterion: the maximum number of iterations. min-step = 0.1; double in (0, inf) Minimal absolute step size. xtola = 0.01; double in [0, inf) Stop if the inf-norm of the change applied to x is below this value.. gdsq Gradient descent with quadratic step estimation, supported parameters are: ftolr = 0; double in [0, inf) Stop if the relative change of the criterion is below.. gtola = 0; double in [0, inf) Stop if the inf-norm of the gradient is below this value.. maxiter = 100; uint in [1, inf) Stopping criterion: the maximum number of iterations. scale = 2; double in (1, inf) Fallback fixed step size scaling. step = 0.1; double in (0, inf) Initial step size. xtola = 0; double in [0, inf) Stop if the inf-norm of x-update is below this value.. gsl optimizer plugin based on the multimin optimizers of the GNU Scientific Library (GSL) https://www.gnu.org/software/gsl/, supported parameters are: eps = 0.01; double in (0, inf) gradient based optimizers: stop when |grad| < eps, simplex: stop when simplex size < eps.. iter = 100; uint in [1, inf) maximum number of iterations. opt = gd; dict Specific optimizer to be used.. Supported values are: bfgs Broyden-Fletcher-Goldfarb-Shann bfgs2 Broyden-Fletcher-Goldfarb-Shann (most efficient version) cg-fr Flecher-Reeves conjugate gradient algorithm gd Gradient descent. simplex Simplex algorithm of Nelder and Mead cg-pr Polak-Ribiere conjugate gradient algorithm step = 0.001; double in (0, inf) initial step size. tol = 0.1; double in (0, inf) some tolerance parameter. nlopt Minimizer algorithms using the NLOPT library, for a description of the optimizers please see 'http://ab- initio.mit.edu/wiki/index.php/NLopt_Algorithms', supported parameters are: ftola = 0; double in [0, inf) Stopping criterion: the absolute change of the objective value is below this value. ftolr = 0; double in [0, inf) Stopping criterion: the relative change of the objective value is below this value. higher = inf; double Higher boundary (equal for all parameters). local-opt = none; dict local minimization algorithm that may be required for the main minimization algorithm.. Supported values are: gn-orig-direct-l Dividing Rectangles (original implementation, locally biased) gn-direct-l-noscal Dividing Rectangles (unscaled, locally biased) gn-isres Improved Stochastic Ranking Evolution Strategy ld-tnewton Truncated Newton gn-direct-l-rand Dividing Rectangles (locally biased, randomized) ln-newuoa Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation gn-direct-l-rand-noscale Dividing Rectangles (unscaled, locally biased, randomized) gn-orig-direct Dividing Rectangles (original implementation) ld-tnewton-precond Preconditioned Truncated Newton ld-tnewton-restart Truncated Newton with steepest-descent restarting gn-direct Dividing Rectangles ln-neldermead Nelder-Mead simplex algorithm ln-cobyla Constrained Optimization BY Linear Approximation gn-crs2-lm Controlled Random Search with Local Mutation ld-var2 Shifted Limited-Memory Variable-Metric, Rank 2 ld-var1 Shifted Limited-Memory Variable-Metric, Rank 1 ld-mma Method of Moving Asymptotes ld-lbfgs-nocedal None ld-lbfgs Low-storage BFGS gn-direct-l Dividing Rectangles (locally biased) none don't specify algorithm ln-bobyqa Derivative-free Bound-constrained Optimization ln-sbplx Subplex variant of Nelder-Mead ln-newuoa-bound Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation ln-praxis Gradient-free Local Optimization via the Principal-Axis Method gn-direct-noscal Dividing Rectangles (unscaled) ld-tnewton-precond-restart Preconditioned Truncated Newton with steepest-descent restarting lower = -inf; double Lower boundary (equal for all parameters). maxiter = 100; int in [1, inf) Stopping criterion: the maximum number of iterations. opt = ld-lbfgs; dict main minimization algorithm. Supported values are: gn-orig-direct-l Dividing Rectangles (original implementation, locally biased) g-mlsl-lds Multi-Level Single-Linkage (low- discrepancy-sequence, require local gradient based optimization and bounds) gn-direct-l-noscal Dividing Rectangles (unscaled, locally biased) gn-isres Improved Stochastic Ranking Evolution Strategy ld-tnewton Truncated Newton gn-direct-l-rand Dividing Rectangles (locally biased, randomized) ln-newuoa Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation gn-direct-l-rand-noscale Dividing Rectangles (unscaled, locally biased, randomized) gn-orig-direct Dividing Rectangles (original implementation) ld-tnewton-precond Preconditioned Truncated Newton ld-tnewton-restart Truncated Newton with steepest-descent restarting gn-direct Dividing Rectangles auglag-eq Augmented Lagrangian algorithm with equality constraints only ln-neldermead Nelder-Mead simplex algorithm ln-cobyla Constrained Optimization BY Linear Approximation gn-crs2-lm Controlled Random Search with Local Mutation ld-var2 Shifted Limited-Memory Variable-Metric, Rank 2 ld-var1 Shifted Limited-Memory Variable-Metric, Rank 1 ld-mma Method of Moving Asymptotes ld-lbfgs-nocedal None g-mlsl Multi-Level Single-Linkage (require local optimization and bounds) ld-lbfgs Low-storage BFGS gn-direct-l Dividing Rectangles (locally biased) ln-bobyqa Derivative-free Bound-constrained Optimization ln-sbplx Subplex variant of Nelder-Mead ln-newuoa-bound Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation auglag Augmented Lagrangian algorithm ln-praxis Gradient-free Local Optimization via the Principal-Axis Method gn-direct-noscal Dividing Rectangles (unscaled) ld-tnewton-precond-restart Preconditioned Truncated Newton with steepest-descent restarting ld-slsqp Sequential Least-Squares Quadratic Programming step = 0; double in [0, inf) Initial step size for gradient free methods. stop = -inf; double Stopping criterion: function value falls below this value. xtola = 0; double in [0, inf) Stopping criterion: the absolute change of all x-values is below this value. xtolr = 0; double in [0, inf) Stopping criterion: the relative change of all x-values is below this value.
Register the image 'moving.png' to the image 'reference.png' by using a rigid transformation model and ssd as cost function. Write the result to output.png mia-2dimageregistration -i moving.png -r reference.png -o output.png -f rigid image:cost=ssd
Gert Wollny
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