mia-2dseri...oothgradMAD(1)

📦
version 2.4.7 · 4 versions
, in ubuntu resolute, ubuntu stonking

✨ mia-2dseriessmoothgradMAD(1) ✨

General Commands Manual — yes bestie, that's the whole name, we don't abbreviate around here 💅

okay so. sit down, grab a strawberry, because this page is A LOT and I am going to walk you through every single crumb of it. no cap. 🌸

🌈 NAME 🌈

mia-2dseriessmoothgradMAD — evaluate the pixel-wise time-intensity gradient of a 2D image series. that's the vibe. that's the whole job. 🫶

🦋 SYNOPSIS 🦋

mia-2dseriessmoothgradMAD -i <in-file> -o <out-file> [options]
<PLUGINS:2dimage/filter>

💜 DESCRIPTION 💜

so like, picture this: you hand it a bunch of images that are a temporal succession — a lil movie, a series, a glow-up over time 🎬 — and it goes and evaluates the temporal pixel-wise gaussian on them, and then evaluates pixel-wise its MAD.

and oh? you want more control? a spacial pre-filtering may be applied by specifying additional plugins (filter/2dimage). iconic. literally doing the most for you.

🎀 OPTIONS 🎀

  • -i, --in-file (input, required); string — the input segmentation set. yes bestie, it's required, don't come to the function without it 🙈
  • -o, --out-file (output, required); io — output file name. for supported file types see PLUGINS:2dimage/io. also required, also mandatory, also no skipping ✨
  • -k, --skip=0 — skip files at the beginning. just, like, walk past them 💅
  • -e, --enlarge-boundary=5 — enlarge the cropbox by this number of pixels.
  • -c, --crop — crop the image before running statistics. tidy.
  • -g, --gauss=1 — gauss filter width for smoothing the gradient.

🌟 Help & Info 🌟

  • -V, --verbose=warning — verbosity of output, print messages of given level and higher priorities. supported priorities starting at 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. bye bestie 👋

🧚 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). let it cook. 🔥
ngl the spelling "Maxiumum" is in the original and I am not touching it. it's canon now. 💀

🔥 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.
  • scharr — this plugin provides the 1D folding kernel for the Scharr gradient filter.
    • (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: 2dimage/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: 2dimage/filter 👑 (the big one, bestie)

  • adaptmed — 2D image adaptive median filter, supported parameters are:
    • w = 2; int in [1, inf) — half filter width.
  • admean — an adaptive mean filter that works like a normal mean filter, if the intensity variation within the filter mask is lower then the intensity variation in the whole image, that the uses a special formula if the local variation is higher then the image intensity variation., supported parameters are:
    • w = 1; int in [1, inf) — half filter width.
  • aniso — 2D Anisotropic image filter, supported parameters are:
    • epsilon = 1; float in (0, inf) — iteration change threshold.
    • iter = 100; int in [1, 10000] — number of iterations.
    • k = -1; float in [0, 100] — k the noise threshold (<=0 -> adaptive).
    • n = 8; set — neighbourhood. Supported values are: ( 4, 8, )
    • psi = tuckey; dict — edge stopping function. Supported values are:
      • tuckey — tukey stopping function
      • pm1 — stopping function 1
      • guess — test stopping function
      • pm2 — stopping function 2
  • 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; set — a hint at the main image content. Supported values are: ( black, white, )
    • shape = [sphere:r=2]; factory — structuring element. For supported plug-ins see PLUGINS:2dimage/shape
  • combiner — combine two images with the given combiner operator. if 'reverse' is set to false, the first operator is the image passed through the filter pipeline, and the second image is loaded from the file given with the 'image' parameter the moment the filter is run., supported parameters are:
    • image =(input, required, io) — second image that is needed in the combiner. For supported file types see PLUGINS:2dimage/io
    • op =(required, factory) — Image combiner to be applied to the images. For supported plug-ins see PLUGINS:2dimage/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:
      • copy — copy data when converting
      • linear — apply linear transformation x -> a*x+b
      • range — apply linear transformation that maps the input data type range to the output data type range
      • opt — apply a linear transformation that maps the real input range to the full output range
      • 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:
      • bit — binary data
      • sbyte — signed 8 bit
      • ubyte — unsigned 8 bit
      • sshort — signed 16 bit
      • ushort — unsigned 16 bit
      • sint — signed 32 bit
      • uint — unsigned 32 bit
      • slong — signed 64 bit
      • ulong — unsigned 64 bit
      • float — floating point 32 bit
      • double — floating point 64 bit
      • none — no pixel type defined
  • crop — Crop a region of an image, the region is always clamped to the original image size., supported parameters are:
    • end = [[-1,-1]]; streamable — end of crop region.
    • start = [[0,0]]; streamable — start of crop region.
  • dilate — 2d image stack dilate filter, supported parameters are:
    • hint = black; set — a hint at the main image content. Supported values are: ( black, white, )
    • shape = [sphere:r=2]; factory — structuring element. For supported plug-ins see PLUGINS:2dimage/shape
  • distance — 2D image distance filter, evaluates the distance map for a binary mask.
    • (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]]; 2dbounds — blocksize.
    • bx = 1; uint in [1, inf) — blocksize in x direction.
    • by = 1; uint in [1, inf) — blocksize in y direction.
    • kernel = gauss; factory — smoothing filter kernel to be applied, the size of the filter is estimated based on the blocksize.. For supported plug-ins see PLUGINS:1d/spacialkernel
  • erode — 2d image stack erode filter, supported parameters are:
    • hint = black; set — a hint at the main image content. Supported values are: ( black, white, )
    • shape = [sphere:r=2]; factory — structuring element. For supported plug-ins see PLUGINS:2dimage/shape
  • gauss — isotropic 2D gauss filter, supported parameters are:
    • w = 1; int in [0, inf) — filter width parameter.
  • gradnorm — 2D image to gradient norm filter, supported parameters are:
    • normalize = 0; bool — Normalize the gradient norms to range [0,1]..
  • invert — intensity invert filter
    • (no parameters)
  • kmeans — 2D 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 — Label connected components in a binary 2D image., supported parameters are:
    • n = 4n; factory — Neighborhood mask to describe connectivity.. For supported plug-ins see PLUGINS:2dimage/shape

💖 labelmap 💖

Image filter that remaps label id's — so basically a glow-up for your labels ✨ but heads up bestie, it only works on images with integer valued intensities/labels. Supported parameters are:

  • map =(input, required, string) — the label mapping file, periodt.

🌸 labelscale 🌸

A filter that only creates output voxels that already exist in the input image. The scaling is done via a voting algorithm — it picks the target pixel value based on the highest pixel count of a certain label in the corresponding source region. And if the region has two labels with the same count?? 😭 bestie the one with the lower number wins. Supported parameters are:

  • out-size =(required, 2dbounds) — target size, given as two comma separated values.

🦋 load 🦋

Loads the input image from a file and uses it to replace the current image in the pipeline, iykyk. Supported parameters are:

  • file =(input, required, io) — name of the input file to load from. For supported file types see PLUGINS:2dimage/io.

🎀 mask 🎀

2D masking, and no cap: one of the two input images must be of type bit. Supported parameters are:

  • fill = min; dict — fill style for pixels outside of the mask. Supported values are:
    • min — set values outside the mask to the minimum value found in the image.
    • zero — set the values outside the mask to zero.
    • max — set the values outside the mask to the maximum value found in the image.
  • input =(input, required, io) — second input image file name. For supported file types see PLUGINS:2dimage/io.
  • inverse = 0; bool — set to true to use the inverse of the mask for masking.

⭐ maxflow ⭐

This filter implements the... uses the max-flow min-cut algorithm for image segmentation. Supported parameters are:

  • sink-flow =(input, required, io) — image of float type to define the per-pixel flow to the sink. For supported file types see PLUGINS:2dimage/io.
  • source-flow =(input, required, io) — image of float type to define the per-pixel flow to the source. For supported file types see PLUGINS:2dimage/io.

🌈 mean 🌈

2D image mean filter, so cozy fr. Supported parameters are:

  • w = 1; int in [1, inf) — half filter width.

🔥 meanvar 🔥

Filter that evaluates simultaneously the pixel wise mean and the variance of an image in a given window. Pixel intensities below the given threshold get ignored, and at their locations the output mean and variation are set to zero. The mean intensity image is passed directly as a float image to the pipeline, and the variation image is saved to a file given with the varfile parameter. Supported parameters are:

  • thresh = 0; double in [0, inf) — intensity thresholding parameter: pixels with intensities below this threshold get set to zero, and also aren't used when evaluating mean and variation.
  • varfile =(output, required, io) — name of the output file to save the variation image to. For supported file types see PLUGINS:2dimage/io.
  • w = 1; uint in [1, inf) — filter width parameter.

👑 median 👑

2D image median filter, the icon it is. Supported parameters are:

  • w = 1; int in [1, inf) — half filter width.

💜 medianmad 💜

Filter that evaluates simultaneously the pixel wise median and the median absolute deviation (MAD) of an image in a given window. Pixel intensities below the given threshold get ignored, and at their locations the output median and MAD get set to zero. The median intensity image is passed directly to the pipeline, and the variation image is saved to a file given with the varfile parameter. Both output images have the same pixel type as the input image. Supported parameters are:

  • madfile =(output, required, io) — name of the output file to save the median absolute deviation image to. For supported file types see PLUGINS:2dimage/io.
  • thresh = 0; double in [0, inf) — intensity thresholding parameter: pixels with intensities below this threshold get set to zero, and also aren't used when evaluating mean and variation.
  • w = 1; uint in [1, inf) — filter width parameter.

🧚 mlv 🧚

Mean of Least Variance 2D image filter. Supported parameters are:

  • w = 1; int in [1, inf) — filter width parameter.

✨ ngfnorm ✨

2D image to normalized-gradient-field-norm filter. Like, that's the whole vibe:

(no parameters)

🍓 noise 🍓

2D image noise filter: adds additive or modulated noise to an image. Supported parameters are:

  • g = [gauss:mu=0,sigma=10]; factory — noise generator. For supported plug-ins see PLUGINS:generator/noise.
  • mod = 0; bool — additive or modulated noise.

🫶 open 🫶

Morphological open, slay. Supported parameters are:

  • hint = black; set — a hint at the main image content. Supported values are: ( black, white, ).
  • shape = [sphere:r=2]; factory — structuring element. For supported plug-ins see PLUGINS:2dimage/shape.

💅 pruning 💅

Morphological pruning. Heads up: pruning until convergence will erase all pixels but closed loops. Supported parameters are:

  • iter = 0; int in [1, 1000000] — number of iterations to run, with 0 meaning until convergence.

🌸 regiongrow 🌸

Region growing, starting from a seed until... only along increasing gradients. Supported parameters are:

  • n = 8n; factory — neighborhood shape. For supported plug-ins see PLUGINS:2dimage/shape.
  • seed =(input, required, io) — seed image (bit valued). For supported file types see PLUGINS:2dimage/io.

🎀 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 🦋

2D image downscale filter. Supported parameters are:

  • interp = [bspline:d=3]; factory — interpolation method to be used. For supported plug-ins see PLUGINS:1d/splinekernel.
  • s = [[0,0]]; 2dbounds — target size as 2D vector.
  • sx = 0; uint in [0, inf) — target size in x direction, where 0 means use input size.
  • sy = 0; uint in [0, inf) — target size in y direction, where 0 means use input size.

⭐ selectbig ⭐

2D label select biggest component filter, no notes needed:

(no parameters)

🔥 sepconv 🔥

2D image intensity separable 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.

💜 shmean 💜

2D image filter that evaluates the mean over a given neighborhood shape. Supported parameters are:

  • shape = 8n; factory — neighborhood shape to evaluate the mean. For supported plug-ins see PLUGINS:2dimage/shape.

🌈 sobel 🌈

The 2D Sobel filter for gradient evaluation. Important though bestie: the output pixel type of the filtered image is the same as the input pixel type, so converting the input beforehand to a floating point valued image is recommendable, fr fr. Supported parameters are:

  • dir = x; dict — gradient direction. Supported values are:
    • x — gradient in x-direction.
    • y — gradient in y-direction.

👑 sort-label 👑

This plug-in sorts the labels of a gray-scale image so that the lowest label value corresponds to the label with the most pixels. The background (0) is not touched. That's it:

(no parameters)

🧚 sws 🧚

seeded watershed. The algorithm extracts exactly as many regions 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 watershed region growing. For supported plug-ins see PLUGINS:2dimage/shape.
  • seed =(input, required, string) — seed input image containing the labels for the initial regions.

🍓 tee 🍓

Saves the input image to a file and also passes it through to the next filter. Supported parameters are:

  • file =(output, required, io) — name of the output file to save the image to. For supported file types see PLUGINS:2dimage/io.

✨ thinning ✨

Morphological thinning. Thinning until convergence will result in a 8-connected skeleton. Supported parameters are:

  • iter = 0; int in [1, 1000000] — number of iterations to run, with 0 meaning until convergence.

💅 thresh 💅

This filter sets all pixels of an image to zero that fall below a certain threshold, and whose neighbours in a given neighborhood shape also fall below this threshold. Supported parameters are:

  • shape = 4n; factory — neighborhood shape to take into account. For supported plug-ins see PLUGINS:2dimage/shape.
  • thresh = 5; double — the threshold value.
  • tmean — a 2D image thresholded tmean filter 💅 okay so listen: the output pixel value is zero if the input pixel value is below the given threshold. Otherwise? The pixels in the evaluation windows only count if their input pixel intensity is above the threshold. It's giving picky, but in a good way ✨ Here's what you can tweak:
    • t = 0; float — the threshold, bestie. Pixels below it don't get taken into account. Periodt.
    • w = 1; int in [1, inf) — the half filter width 🎀
  • transform — transforms the input image with the transformation you hand it, no questions asked 💫 supported parameters are literally just this one:
    • file =(input, required, io) — the name of the file holding the transformation. For the file types that are actually supported, go peek at PLUGINS:2dtransform/io 🦋
  • ws — basic watershead segmentation 🌊 (yes it's spelled like that, iykyk). Supported parameters:
    • evalgrad = 0; bool — set this to 1 if your input image does not represent a gradient norm image. Just saying.
    • mark = 0; bool — set to 1 and it marks the segmented watersheds with a special gray scale value 👑
    • n = [sphere:r=1]; factory — the neighborhood used for watershead region growing. For the plug-ins that work here, see PLUGINS:2dimage/shape 🌸
    • thresh = 0; float in [0, 1) — relative gradient norm threshold, okurrr. The actual threshold value is thresh * (max_grad - min_grad) + min_grad. Bassins separated by gradients with a lower norm will be joined. That's just facts 💀

🌈 PLUGINS: 2dimage/io 🌈

All the ways to read and write your 2D images, bestie. Choose your fighter:

  • bmp — BMP 2D-image input/output support 💖 This plug-in reads and writes 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. No cap.
    • 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/shape 🧚

Shapes for your kernels and neighborhoods, because a girl needs options:

  • 1n — a shape that only contains the central point 🎯
    • (no parameters) — yeah, nothing to configure, it's minimalism
  • 4n — 4n neighborhood 2D shape 🍓
    • (no parameters)
  • 8n — 8n neighborhood 2D shape ⭐
    • (no parameters)
  • rectangle — rectangle shape mask creator, supported parameters are:
    • fill = 1; bool — create a filled shape 💖
    • height = 2; int in [1, inf) — height of rectangle 📏
    • width = 2; int in [1, inf) — width of rectangle 📐
  • sphere — closed spherical neighborhood shape of radius r. Supported parameters are:
    • r = 2; float in (0, inf) — sphere radius 🔮
  • square — square shape mask creator, supported parameters are:
    • fill = 1; bool — create a filled shape ✅
    • width = 2; int in [1, inf) — width of rectangle 📐 (yes, it says rectangle, we don't make the rules)

💜 PLUGINS: 2dtransform/io 💜

Ways to save and load your 2D transformations:

  • bbs — Binary (non-portable) serialized IO of 2D transformations ⚠️ non-portable, so don't go shipping these files around like they're universal, bestie.
    • 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: generator/noise 🔥

Randomness, but make it fashion:

  • gauss — this noise generator creates random values that are distributed according to a Gaussien distribution by using the Box-Muller transformation 🧠✨ supported parameters are:
    • mu = 0; float — mean of distribution.
    • seed = 0; uint in [0, inf) — set random seed (0 = init based on system time) ⏰
    • sigma = 1; float in (0, inf) — standard derivation of distribution.
  • uniform — uniform noise generator using C stdlib rand() 🎲 supported parameters are:
    • a = 0; float — lower bound if noise range.
    • b = 1; float — higher bound if noise range.
    • seed = 0; uint in [0, inf) — set random seed (0 = init based on system time) ⏰

✨ EXAMPLE ✨

Okay so this is the moment. We wanna evaluate the MAD-image of the bounding box surrounding the segmentation from a series segment.set after applying a temporal Gaussian filter of width 5 🌊 No spacial filtering will be applied, okay? The bounding box will be enlarged by 3 pixels in all directions. And we store the image in OpenEXR format 💖

mia-2dseriessmoothgradMAD -i segment.set -o mad.exr -g 2 -c -e 3

👑 AUTHOR(s) 👑

Gert Wollny 💅 the icon, the moment, the legend.

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 on this one, bestie 🫶 it's USER COMMANDS, version v2.4.7, and the page date is mia-2dseri...oothgradMAD(1) — go read it again if you need to, mwah 💋

📦 where this page comes from