mia-2dseries2dordermedian(1)

NAME
mia-2dseries2dordermedian - Evaluate time-intensity median 2nd order derivative of a series.

SYNOPSIS
mia-2dseries2dordermedian -i <in-file> -o <out-file> [options] <PLUGINS:2dimage/filter>

DESCRIPTION
mia-2dseries2dordermedian This program evaluates the pixel-wise median of the absolute values of the gauss filtered 2nd order temporal derivative of a series of images. In addition, it can be used to output the time-intensity curve of a given pixel.The program supports slice-wise spacial pre-filtering by giving additional filters as free parameters (filter/2dimage).

OPTIONS

    -i --in-file=(input, required); string
        input segmentation set

    -o --out-file=(output, required); io
        output image name
         For supported file types see PLUGINS:2dimage/io

    -k --skip=0
        Skip files at the beginning

    -e --enlarge-boundary=5
        Enlarge cropbox by number of pixels

    -c --crop
        crop image before running statistics

    -g --gauss=1
        gauss filter width for moothing the gradient

    --itc-file=
        intensity time curve output file

    --itc-loc=[0,0]
        intensity time curve output pixel coordinates

  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

  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).

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

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 to remap label id's. Only applicable to images with integer valued intensities/labels., supported parameters are:

    map =(input, required, string)
        Label mapping file.

labelscale
    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
    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

mask
    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.

maxflow
    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

mean
    2D image mean filter, supported parameters are:

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

meanvar
    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.

median
    2D image median filter, supported parameters are:

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

medianmad
    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.

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-gradiend-field-norm filter

    (no parameters)

noise
    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.

open
    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

pruning
    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.

regiongrow
    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

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, 0: use input size.

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

selectbig
    2D label select biggest component filter

    (no parameters)

sepconv
    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

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. 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

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

    (no parameters)

sws 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 labels for the initial regions.

tee 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

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, 0=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 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.

tmean
    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
    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

ws  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.

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/shape

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
    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.

PLUGINS: 2dtransform/io

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

PLUGINS: generator/noise

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
Evaluate the median of the 2nd order derivative of the series given in segmentation set segment.set after filtering with a Gaussian of width 3. In addition write the time intensity curve of pixel <128,64> to curve.txt.

mia-2dseries2dordermedian -i segment.set -o gradmedian.exr -g 1 --itc-file curve.txt --itc-loc "<128,64>"

AUTHOR(s)
Gert Wollny

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'.