mia-2dseriesgradMAD(1)
NAME
mia-2dseriesgradMAD - Evaluate the time-intensity gradient MAD in a series of images.
SYNOPSIS
mia-2dseriesgradMAD -i <in-file> -o <out-file> [options] <PLUGINS:2dimage/filter>
DESCRIPTION
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).
OPTIONS
-i --in-file=(input, required); string
input segmentation set
-o --out-file=(output, required); io
output file name For supported file types see
PLUGINS:2dimage/io
-k --skip=0
Skip files at the beginningSkip files at the beginning
-e --enlarge-boundary=5
Enlarge cropbox by number of pixelsEnlarge cropbox by
number of pixels
-c --crop
crop image before running statistics
Help & Info
-V --verbose=warning
verbosity of output, print messages of given level and
higher priorities. Supported priorities starting at
lowest level are:
info Low level messages
trace Function call trace
fail Report test failures
warning Warnings
error Report errors
debug Debug output
message Normal messages
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).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:
guess test stopping function
tuckey tukey stopping function
pm1 stopping function 1
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, string)
second image that is needed in the combiner.
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:
opt apply a linear transformation that maps the
real input range to the full output range
range apply linear transformation that maps the
input data type range to the output data type range
copy copy data when converting
linear apply linear transformation x -> a*x+b
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:
none no pixel type defined
float floating point 32 bit
sbyte signed 8 bit
ulong unsigned 64 bit
double floating point 64 bit
sint signed 32 bit
ushort unsigned 16 bit
sshort signed 16 bit
uint unsigned 32 bit
slong signed 64 bit
bit binary data
ubyte unsigned 8 bit
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; string
smoothing filter kernel to be applied, the size of the
filter is estimated based on the blocksize..
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, string)
name of the input file to load from..
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:
max set values outside the mask to the maximum
value found in the image..
zero set the values outside the mask to zero.
min set values outside the mask to the minimum
value found in the image.
input =(input, required, string)
second input image file name.
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, string)
Image of float type to define the per-pixel flow to the
sink.
source-flow =(input, required, string)
Image of float type to define the per-pixel flow to the
source.
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, string)
name of the output file to save the variation image
too..
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, string)
name of the output file to save the median absolute
deviation image too..
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, string)
seed image (bit valued).
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:
y gradient in y-direction
x gradient in x-direction
sort-label
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)
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 lables 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, string)
name of the output file to save the image too..
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, string)
Name of the file containing the transformation..
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 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
AUTHOR(s)
Gert Wollny
COPYRIGHT
This software is Copyright (c) 19992015 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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