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mia-2dseriesgradMAD(1) General Commands Manual mia-2dseriesgradMAD(1)

mia-2dseriesgradMAD - Evaluate the time-intensity gradient MAD in a series of images.

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

mia-2dseriesgradMAD Given a set of images of temporal sucession, evaluates the pixel-wise temporal gradient and then its median average distance (MAD) and stores the result in an image. Spacial pre-filtering may be applied as given additional plugin(s) (filter/2dimage).

input segmentation set

output file name
For supported file types see PLUGINS:2dimage/io
Skip files at the beginning

Enlarge cropbox by number of pixels

crop image before running statistics

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
print copyright information

print this help

-? --usage
print a short help

print the version number and exit

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).

Central difference filter kernel, mirror boundary conditions are used.

(no parameters)
spacial Gauss filter kernel, supported parameters are:

w = 1; uint in [0, inf)
half filter width.

This plugin provides the 1D folding kernel for the Scharr gradient filter

(no parameters)

B-spline kernel creation , supported parameters are:

d = 3; int in [0, 5]
Spline degree.

OMoms-spline kernel creation, supported parameters are:

d = 3; int in [3, 3]
Spline degree.

Image combiner 'absdiff'

(no parameters)
Image combiner 'add'

(no parameters)
Image combiner 'div'

(no parameters)
Image combiner 'mul'

(no parameters)
Image combiner 'sub'

(no parameters)

2D image adaptive median filter, supported parameters are:

w = 2; int in [1, inf)
half filter width.

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.

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:
pm1 ‐ stopping function 1
pm2 ‐ stopping function 2
tuckey ‐ tukey stopping function
guess ‐ test stopping function

intensity bandpass filter, supported parameters are:

max = 3.40282e+38; float
maximum of the band.

min = 0; float
minimum of the band.

image binarize filter, supported parameters are:

max = 3.40282e+38; float
maximum of accepted range.

min = 0; float
minimum of accepted range.

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

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.

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 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.

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

2D image distance filter, evaluates the distance map for a binary mask.

(no parameters)
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

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

isotropic 2D gauss filter, supported parameters are:

w = 1; int in [0, inf)
filter width parameter.

2D image to gradient norm filter, supported parameters are:

normalize = 0; bool
Normalize the gradient norms to range [0,1]..

intensity invert filter

(no parameters)
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 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

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.

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, 2dbounds)
target size given as two coma separated values.

Load the input image from a file and use it to replace the current image in the pipeline., supported parameters are:

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

2D masking, one of the two input images must by 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 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.

This filter implements the uses the max-flow min-cut algorithmfor 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

2D image mean filter, supported parameters are:

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

Filter that evaluates simultaniously the pixel wise mean and the variance of an image in a given window. Pixel intensities below the given threshold will be ignored and at their loctions the output mean and variation are set to zero. The mean intensity image is directly passed as float image to the pipeline, 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 will be set to zero, and also not used when evaluating mean and variation.

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

w = 1; uint in [1, inf)
filter width parameter.

2D image median filter, supported parameters are:

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

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

madfile =(output, required, io)
name of the output file to save the median absolute deviation image too.. For supported file types see PLUGINS:2dimage/io

thresh = 0; double in [0, inf)
Intensity thresholding parameter: Pixels with intensities below this threshold will be set to zero, and also not used when evaluating mean and variation.

w = 1; uint in [1, inf)
filter width parameter.

Mean of Least Variance 2D image filter, supported parameters are:

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

2D image to normalized-gradiend-field-norm filter

(no parameters)
2D image noise filter: add 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.

morphological open, 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

Morphological pruning. Pruning until convergence will erase all pixels but closed loops., supported parameters are:

iter = 0; int in [1, 1000000]
Number of iterations to run, 0=until convergence.

Region growing startin 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

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.

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, 0: use input size.

sy = 0; uint in [0, inf)
target size in y direction, 0: use input size.

2D label select biggest component filter

(no parameters)
2D 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

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

The 2D Sobel filter for gradient evaluation. Note that 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., supported parameters are:

dir = x; dict
Gradient direction. Supported values are:
x ‐ gradient in x-direction
y ‐ gradient in y-direction

This plug-in sorts the labels of a gray-scale image so that the lowest label value corresponts to the lable with themost pixels. The background (0) is not touched

(no parameters)
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:2dimage/shape

seed =(input, required, string)
seed input image containing the lables for the initial regions.

Save the input image to a file and also pass it through to the next filter, supported parameters are:

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

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, 0=until convergence.

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 a 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.

2D image thresholded tmean filter: The output pixel value is zero if the input pixel value is below the given threshold, otherwise the pixels in the evaluation windows are only considered if the input pixel intensity is above the threshold., supported parameters are:

t = 0; float
Threshold for pixels not to take into account.

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

Transform the input image with the given transformation., supported parameters are:

file =(input, required, io)
Name of the file containing the transformation.. For supported file types see PLUGINS:2dtransform/io

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:2dimage/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.

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

Virtual IO to and from the internal data pool

Recognized file extensions: .@

2D image io for DICOM

Recognized file extensions: .DCM, .dcm

Supported element types:
signed 16 bit, unsigned 16 bit

a 2dimage io plugin for OpenEXR images

Recognized file extensions: .EXR, .exr

Supported element types:
unsigned 32 bit, floating point 32 bit

a 2dimage io plugin for jpeg gray scale images

Recognized file extensions: .JPEG, .JPG, .jpeg, .jpg

Supported element types:
unsigned 8 bit

a 2dimage io plugin for png images

Recognized file extensions: .PNG, .png

Supported element types:
binary data, unsigned 8 bit, unsigned 16 bit

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

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

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

1n
A shape that only contains the central point

(no parameters)
4n
4n neighborhood 2D shape

(no parameters)
8n
8n neighborhood 2D shape

(no parameters)
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.

Closed spherical neighborhood shape of radius r., supported parameters are:

r = 2; float in (0, inf)
sphere radius.

square shape mask creator, supported parameters are:

fill = 1; bool
create a filled shape.

width = 2; int in [1, inf)
width of rectangle.

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

Recognized file extensions: .bbs

Virtual IO to and from the internal data pool

Recognized file extensions: .@

Vista storage of 2D transformations

Recognized file extensions: .v2dt

XML serialized IO of 2D transformations

Recognized file extensions: .x2dt

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 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).

Evaluate the MAD-image of the bounding box surrounding the segmentation from a series segment.set. No spacial filtering will be applied. The bounding box will be enlarged by 3 pixels in all directions. Store the image in OpenEXR format.

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

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'.

v2.4.7 USER COMMANDS