mia-2dmulti-force(1)

🌸 mia-2dmulti-force(1) 🌸

okay so bestie, welcome to mia-2dmulti-force(1) from the General Commands Manual — yes that's a whole mouthful, we're gonna get through it together, grab a snack 🍓

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✨ NAME ✨

mia-2dmulti-force — Registration force between two images

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🦋 SYNOPSIS 🦋

mia-2dmulti-force -o <out-file> [options] <PLUGINS:2dimage/fullcost>

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💖 DESCRIPTION 💖

so mia-2dmulti-force literally evaluates the 2D image cost force norm image of a given cost function set. that's the whole vibe. the input images must be the same dimensions and gray scale (whatever bit-depth, she's flexible like that 💅).

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👑 OPTIONS 👑

• -o, --out-file=(output, required); io — the output norm image. for the file types she supports, peek at PLUGINS:2dimage/io below 🫶

🎀 Help & Info 🎀

• -V, --verbose=warning — verbosity of output, prints messages of the given level and higher priorities. 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 — 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). yes, "Maxiumum" — we keep the typo, it's part of the lore, no cap 🙈

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🧚 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.

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💜 PLUGINS: 2dimage/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.

lsd

Least-Squares Distance measure

• (no parameters) — she's low maintenance, we love that

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.

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🌈 PLUGINS: 2dimage/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: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., 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, 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. 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, 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.

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⭐ PLUGINS: 2dimage/io ⭐

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

💖 PLUGINS: 2dimage/maskedcost 💖

okay so these are the cost functions that know how to do the masked thing — same energy as their unmasked besties, but they respect a mask. 🌸

lncc — local normalized cross correlation with masking support. ✨ supported parameters are:

• w = 5; uint in [1, 256] — the half width of the window used for evaluating the localized cross correlation. like, half, so the actual window is bigger than this number, keep that in mind bestie 🫶

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. yes it's literally trimming the extremes, slay 💅
• 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

ngl the default mkernel is bspline:d=3 and rkernel is bspline:d=0 — they're not the same, that's not a typo, that's just how it is. 👀

ncc — normalized cross correlation with masking support.

• (no parameters) — it's giving low maintenance, iconic ✨

ssd — Sum of squared differences with masking.

• (no parameters) — also no parameters, we love a chill one 💖

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🌈 EXAMPLE 🌈

Evaluate the force normimage weighted sum of costs SSD and NGF of image1.v and image2.v. and store the result to force.v.

mia-2dmulti-force -o force.v
        image:cost=ssd,src=image1.v,ref=image2.v,weight=0.1
        image:cost=ngf,src=image1.v,ref=image2.v,weight=2.0

like, two costs, two weights, one output file — main character behavior fr fr 🔥

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👑 AUTHOR(s) 👑

Gert Wollny

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🎀 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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and that's the whole page bestie!! 🦋 the footer says this is mia-2dmulti-force(1), living in USER COMMANDS, and the version is v2.4.7 — so if you're quoting this at anybody, that's the vibe check receipt. go force some images, you absolute legend 💜✨