mia-2dimageregistration(1)

📦
version 2.4.7 · 2 versions
, in ubuntu noble, ubuntu resolute, ubuntu stonking

💖 mia-2dimageregistration(1) 💖

General Commands Manual, but make it cute. this is the page for mia-2dimageregistration and bestie it's serving serious registration energy ✨

🌸 NAME

mia-2dimageregistration — runs a 2d image registration. that's it, that's the vibe 💅

🦋 SYNOPSIS

mia-2dimageregistration -i <in-image> -r <ref-image> -t <transformation>
[options] <PLUGINS:2dimage/fullcost>

✨ DESCRIPTION

okay so mia-2dimageregistration literally runs a registration of two images 🫶 it takes a transformation from whatever transformation model you gave it and optimizes the heck out of it, using certain cost measures — and those cost measures are handed in as free parameters. the way that it just… aligns things? obsessed. 💜

💅 OPTIONS 💅

📁 File-IO

  • -i, --in-image=(input, required); io — the test image, aka the one that's getting registered 🎀 for supported file types see PLUGINS:2dimage/io
  • -r, --ref-image=(input, required); io — the reference image, aka the one everything gets registered to 🌸 for supported file types see PLUGINS:2dimage/io
  • -o, --out-image=(output); io — the registered output image that falls out at the end 💖 for supported file types see PLUGINS:2dimage/io
  • -t, --transformation=(output, required); io — the output transformation that comprises the registration 👑 for supported file types see PLUGINS:2dtransform/io

🧚 Help & Info

  • -V, --verbose=warning — how chatty the output is. it prints messages of the given level and every higher priority. the supported priorities, starting at the lowest level, are:
    • trace — function call trace
    • debug — debug output
    • info — low level messages
    • message — normal messages
    • warning — warnings
    • fail — report test failures
    • error — report errors
    • fatal — report only fatal errors
  • --copyright — prints the copyright information 📜
  • -h, --help — prints this help 🫶
  • -?, --usage — prints a short help
  • --version — prints the version number and then exits, main character exit 💫

🎀 Parameters

  • -l, --levels=3 — number of multi-resolution levels 🪜
  • -O, --optimizer=gsl:opt=gd,step=0.1 — the optimizer used for minimization 🔥 for supported plugins see PLUGINS:minimizer/singlecost
  • -R, --refiner= — the optimizer used for refinement after the main optimizer was called, like a lil glow-up pass ✨ for supported plugins see PLUGINS:minimizer/singlecost
  • -f, --transForm=spline — transformation type 🌈 for supported plugins see PLUGINS:2dimage/transform

⚙️ Processing

  • --threads=-1 — maximum number of threads to use for processing. this number should be lower than or equal to the number of logical processor cores in the machine. (-1 means automatic estimation) 🧵

🦋 PLUGINS: 1d/splinebc

mirror 🪞

Spline interpolation boundary conditions that mirror right on the boundary.

(no parameters) — yeah, it's just that simple, no notes 💅

repeat 🔁

Spline interpolation boundary conditions that repeats the value at the boundary.

(no parameters) — nothing to tune, iconic behaviour 🫶

zero 0️⃣

Spline interpolation boundary conditions that assumes zero for values outside.

(no parameters) — no knobs, no drama 🌸

🌟 PLUGINS: 1d/splinekernel

bspline 🌀

B-spline kernel creation. here's what you can set:

  • d = 3; int in [0, 5] — spline degree.

omoms 💫

OMoms-spline kernel creation. here's what you can set:

  • d = 3; int in [3, 3] — spline degree.

💜 PLUGINS: 2dimage/cost

lncc 🔍

local normalized cross correlation, and yes it has masking support. parameters:

  • 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) — nothing to tweak, it just measures 🎀

mi 🎲

Spline parzen based mutual information. parameters:

  • 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 the 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 the reference image parzen hinstogram. for supported plug-ins see PLUGINS:1d/splinekernel

ncc 📐

normalized cross correlation.

(no parameters) — clean, minimal, slay ✨

ngf 🌈

this one evaluates the image similarity based on normalized gradient fields, and there are various evaluation kernels available. parameters:

  • eval = ds; dict — plugin subtype. the 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. parameters:

  • 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. parameters:

  • rthresh = 0; double — threshold intensity value for reference image.
  • sthresh = 0; double — threshold intensity value for source image.

👑 PLUGINS: 2dimage/fullcost

image 🖼️

generalized image similarity cost function that also handles multi-resolution processing. the actual similarity measure is given es extra parameter. parameters:

  • cost = ssd; factory — cost function kernel. for supported plug-ins see PLUGINS:2dimage/cost
  • debug = 0; bool — save intermediate resuts for debugging.
  • ref =(input, io) — reference image. for supported file types see PLUGINS:2dimage/io
  • src =(input, io) — study image. for supported file types see PLUGINS:2dimage/io
  • weight = 1; float — weight of cost function.

labelimage 🏷️

similarity cost function that maps labels of two images and handles label-preserving multi-resolution processing. parameters:

  • 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, io) — reference image. for supported file types see PLUGINS:2dimage/io
  • src =(input, io) — study image. for supported file types see PLUGINS:2dimage/io
  • weight = 1; float — weight of cost function.

maskedimage 🎭

generalized masked image similarity cost function that also handles multi-resolution processing.

fair warning bestie: the provided masks should be densly filled regions in multi-resolution procesing, because otherwise the mask information may get lost when downscaling the image 😭 no cap, that's a real one.

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. parameters:

  • cost = ssd; factory — cost function kernel. for supported plug-ins see PLUGINS:2dimage/maskedcost
  • ref =(input, io) — reference image. for supported file types see PLUGINS:2dimage/io
  • ref-mask =(input, io) — reference image mask (binary). for supported file types see PLUGINS:2dimage/io
  • src =(input, io) — study image. for supported file types see PLUGINS:2dimage/io
  • src-mask =(input, io) — study image mask (binary). for supported file types see PLUGINS:2dimage/io
  • weight = 1; float — weight of cost function.

🌈 PLUGINS: 2dimage/io 💾

ok so these are the little file-format besties that let your 2D images go in and out of the program. each one handles its own ✨vibes✨ of file extensions and pixel types, so let's go:

🎀 bmp

BMP 2D-image input/output support. this plugin can read AND write binary images and 8-bit gray scale images, and it's read-only for 4-bit gray scale images. the color table? completely ignored, bestie — pixel values get taken as literal gray scale values. periodt.

  • Recognized file extensions: .BMP, .bmp
  • Supported element types:
    • binary data, unsigned 8 bit

🎀 datapool

Virtual IO to and from the internal data pool. it's giving teleportation but for data.

  • Recognized file extensions: .@

🎀 dicom

2D image io for DICOM. medical girlies rise up 🩺.

  • Recognized file extensions: .DCM, .dcm
  • Supported element types:
    • signed 16 bit, unsigned 16 bit

🎀 exr

a 2dimage io plugin for OpenEXR images. highkey fancy ✨

  • 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. gray scale only, she's minimal like that 💅

  • Recognized file extensions: .JPEG, .JPG, .jpeg, .jpg
  • Supported element types:
    • unsigned 8 bit

🎀 png

a 2dimage io plugin for png images. the people's princess 👑

  • Recognized file extensions: .PNG, .png
  • Supported element types:
    • binary data, unsigned 8 bit, unsigned 16 bit

🎀 raw

RAW 2D-image output support. output — so keep that in mind, she's not about the reading life.

  • 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. an icon, truly.

  • 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. look at that extension list, she's got range 🌈

  • Recognized file extensions: .-, .V, .VISTA, .v, .vista
  • Supported element types:
    • binary data, signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit, signed 32 bit, unsigned 32 bit, floating point 32 bit, floating point 64 bit

🌸 PLUGINS: 2dimage/maskedcost 🫶

cost functions but make them masked, so you can tell the registration bestie which pixels actually matter. here's the lineup:

🎀 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)
not a single knob to turn. she just works. no cap.

🎀 ssd

Sum of squared differences with masking.

  • (no parameters)
same energy — zero parameters, all confidence 💅

🦋 PLUGINS: 2dimage/transform ✨

these are the transformation besties — the ones that actually describe how your image gets moved, squished, rotated and otherwise manipulated. each one has its own degrees of freedom and its own little pile of 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

💜 PLUGINS: 2dtransform/io 📁

the io besties for 2D transformations specifically (yes, transformations get their own file formats, she's that girl):

🎀 bbs

Binary (non-portable) serialized IO of 2D transformations.

non-portable, bestie. don't be shocked when it doesn't travel well 🚩
  • 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

⭐ PLUGINS: 2dtransform/splinepenalty 🧚

penalty terms for spline transformations — basically the little voice going "girl, not that much wiggle" 🫠

🎀 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. The glow-up version of just walking downhill 💅. The params it fw:

  • ftolr = 0; double in [0, inf) — stop if the relative change of the criterion is below. bestie is done when the numbers stop moving fr.
  • max-step = 2; double in (0, inf) — the maximal absolute step size. biggest stride allowed, no zooming off a cliff 🦋
  • maxiter = 200; uint in [1, inf) — stopping criterion: the maximum number of iterations.
  • min-step = 0.1; double in (0, inf) — the 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. she's guessing her stride size with math ✨. her params:

  • 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/>. classic library core, no cap. the params:

  • 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 — the specific optimizer to be used. supported values are:
    • simplex — Simplex algorithm of Nelder and Mead
    • cg-fr — Flecher-Reeves conjugate gradient algorithm
    • cg-pr — Polak-Ribiere conjugate gradient algorithm
    • bfgs — Broyden-Fletcher-Goldfarb-Shann
    • bfgs2 — Broyden-Fletcher-Goldfarb-Shann (most efficient version)
    • gd — Gradient descent
  • step = 0.001; double in (0, inf) — initial step size.
  • tol = 0.1; double in (0, inf) — some tolerance parameter.

👑 nlopt

Minimizer algorithms using the NLOPT library. for a description of the optimizers please see <http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms> — go read it, it's giving very thorough 💀. supported parameters are:

  • ftola = 0; double in [0, inf) — stopping criterion: the absolute change of the objective value is below this value.
  • ftolr = 0; double in [0, inf) — stopping criterion: the relative change of the objective value is below this value.
  • higher = inf; double — higher boundary (equal for all parameters).
  • local-opt = none; dict — local minimization algorithm that may be required for the main minimization algorithm. supported values are:
    • gn-direct — Dividing Rectangles
    • gn-direct-l — Dividing Rectangles (locally biased)
    • gn-direct-l-rand — Dividing Rectangles (locally biased, randomized)
    • gn-direct-noscal — Dividing Rectangles (unscaled)
    • gn-direct-l-noscal — Dividing Rectangles (unscaled, locally biased)
    • gn-direct-l-rand-noscale — Dividing Rectangles (unscaled, locally biased, randomized)
    • gn-orig-direct — Dividing Rectangles (original implementation)
    • gn-orig-direct-l — Dividing Rectangles (original implementation, locally biased)
    • ld-lbfgs-nocedal — None
    • ld-lbfgs — Low-storage BFGS
    • ln-praxis — Gradient-free Local Optimization via the Principal-Axis Method
    • ld-var1 — Shifted Limited-Memory Variable-Metric, Rank 1
    • ld-var2 — Shifted Limited-Memory Variable-Metric, Rank 2
    • ld-tnewton — Truncated Newton
    • ld-tnewton-restart — Truncated Newton with steepest-descent restarting
    • ld-tnewton-precond — Preconditioned Truncated Newton
    • ld-tnewton-precond-restart — Preconditioned Truncated Newton with steepest-descent restarting
    • gn-crs2-lm — Controlled Random Search with Local Mutation
    • ld-mma — Method of Moving Asymptotes
    • ln-cobyla — Constrained Optimization BY Linear Approximation
    • ln-newuoa — Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation
    • ln-newuoa-bound — Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation
    • ln-neldermead — Nelder-Mead simplex algorithm
    • ln-sbplx — Subplex variant of Nelder-Mead
    • ln-bobyqa — Derivative-free Bound-constrained Optimization
    • gn-isres — Improved Stochastic Ranking Evolution Strategy
    • none — don't specify algorithm
  • lower = -inf; double — lower boundary (equal for all parameters).
  • maxiter = 100; int in [1, inf) — stopping criterion: the maximum number of iterations.
  • opt = ld-lbfgs; dict — main minimization algorithm. supported values are:
    • gn-direct — Dividing Rectangles
    • gn-direct-l — Dividing Rectangles (locally biased)
    • gn-direct-l-rand — Dividing Rectangles (locally biased, randomized)
    • gn-direct-noscal — Dividing Rectangles (unscaled)
    • gn-direct-l-noscal — Dividing Rectangles (unscaled, locally biased)
    • gn-direct-l-rand-noscale — Dividing Rectangles (unscaled, locally biased, randomized)
    • gn-orig-direct — Dividing Rectangles (original implementation)
    • gn-orig-direct-l — Dividing Rectangles (original implementation, locally biased)
    • ld-lbfgs-nocedal — None
    • ld-lbfgs — Low-storage BFGS
    • ln-praxis — Gradient-free Local Optimization via the Principal-Axis Method
    • ld-var1 — Shifted Limited-Memory Variable-Metric, Rank 1
    • ld-var2 — Shifted Limited-Memory Variable-Metric, Rank 2
    • ld-tnewton — Truncated Newton
    • ld-tnewton-restart — Truncated Newton with steepest-descent restarting
    • ld-tnewton-precond — Preconditioned Truncated Newton
    • ld-tnewton-precond-restart — Preconditioned Truncated Newton with steepest-descent restarting
    • gn-crs2-lm — Controlled Random Search with Local Mutation
    • ld-mma — Method of Moving Asymptotes
    • ln-cobyla — Constrained Optimization BY Linear Approximation
    • ln-newuoa — Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation
    • ln-newuoa-bound — Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation
    • ln-neldermead — Nelder-Mead simplex algorithm
    • ln-sbplx — Subplex variant of Nelder-Mead
    • ln-bobyqa — Derivative-free Bound-constrained Optimization
    • gn-isres — Improved Stochastic Ranking Evolution Strategy
    • auglag — Augmented Lagrangian algorithm
    • auglag-eq — Augmented Lagrangian algorithm with equality constraints only
    • g-mlsl — Multi-Level Single-Linkage (require local optimization and bounds)
    • g-mlsl-lds — Multi-Level Single-Linkage (low-discrepancy-sequence, require local gradient based optimization and bounds)
    • ld-slsqp — Sequential Least-Squares Quadratic Programming
  • step = 0; double in [0, inf) — initial step size for gradient free methods.
  • stop = -inf; double — stopping criterion: function value falls below this value.
  • xtola = 0; double in [0, inf) — stopping criterion: the absolute change of all x-values is below this value.
  • xtolr = 0; double in [0, inf) — stopping criterion: the relative change of all x-values is below this value.

🌸 EXAMPLE 🌸

Register 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

🫶 AUTHOR(s) 🫶

Gert Wollny

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.

and that's a wrap bestie, we literally made it to the end of the page 🎉 this whole manual is from the USER COMMANDS section, version v2.4.7 of mia-2dimageregistration(1). go register some images, you got this, mwah 💖✨🦋

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