USER COMMANDS

NAME¶

mia-2dseriessmoothgradMAD - Evaluate pixel-wise time-intensity gradient of a 2D image series.

SYNOPSIS¶

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

DESCRIPTION¶

mia-2dseriessmoothgradMAD Given a set of images of temporal sucession, evaluate the temporal pixel-wise gaussian and evaluate pixel-wise its MAD.A spacial pre-filtering may be applied by specifying additional plugins (filter/2dimage)

OPTIONS¶

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

-o --out-file=(output,required): output file 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

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).

PLUGINS: 1d/spacialkernel¶

gauss: spacial Gauss filter kernel, supported parameters are:

w = 1 (int)

half filter width. in [0, 2147483647]

PLUGINS: 1d/splinekernel¶

bspline: B-spline kernel creation , supported parameters are:

d = 3 (int)

Spline degree. in [0, 5]

omoms: OMoms-spline kernel creation, supported parameters are:

d = 3 (int)

Spline degree. in [3, 3]

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)

half filter width. in [0, 2147483647]

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)

half filter width. in [0, 2147483647]

aniso: 2D Anisotropic image filter, supported parameters are:

epsilon = 1 (float)

iteration change threshold. in [0.001, 100]

iter = 100 (int)

number of iterations. in [1, 10000]

k = -1 (float)

k the noise threshold (<=0 -> adaptive). in [0, 100]

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. in [-3.40282e+38, 3.40282e+38]

min = 0 (float)

minimum of the band. in [-3.40282e+38, 3.40282e+38]

binarize: image binarize filter, supported parameters are:

max = 3.40282e+38 (float)

maximum of accepted range. in [0, 3.40282e+38]

min = 0 (float)

minimum of accepted range. in [0, 3.40282e+38]

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. in [-3.40282e+38, 3.40282e+38]

b = 0 (float)

linear conversion parameter b. in [-3.40282e+38, 3.40282e+38]

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:

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)

blocksize in x direction. in [1, 2147483647]

by = 1 (uint)

blocksize in y direction. in [1, 2147483647]

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)

filter width parameter. in [0, 2147483647]

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)

number of classes. in [2, 255]

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: 2D image filter to remap label id's., supported parameters are:

map =(input,required, string)

Label mapping file.

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:

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, 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.

mean: 2D image mean filter, supported parameters are:

w = 1 (int)

half filter width. in [0, 2147483647]

median: 2D image median filter, supported parameters are:

w = 1 (int)

half filter width. in [0, 2147483647]

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

w = 1 (int)

filter width parameter. in [0, 2147483647]

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)

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

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)

thresh value. in [0, 3.40282e+38]

w = 1 (int)

filter width parameter. in [0, 2147483647]

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)

target size in x direction, 0: use input size. in [0, 4294967295]

sy = 0 (uint)

target size in y direction, 0: use input size. in [0, 4294967295]

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

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, 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)

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

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

shape = 4n (factory)

neighborhood shape to take into account. For supported plug-ins see PLUGINS:2dimage/shape

thresh = 5 (double)

The threshhold value. in [-1.79769e+308, 1.79769e+308]

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)

Relative gradient norm threshold. The actual value threshhold value is thresh * (max_grad - min_grad) + min_grad. Bassins separated by gradients with a lower norm will be joined. in [0, 1]

PLUGINS: 2dimage/io¶

bmp: BMP 2D-image input/output support

Recognized file extensions: .BMP, .bmp

Supported element types:

binary data, unsigned 8 bit, unsigned 16 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)

height of rectangle. in [0, 2147483647]

width = 2 (int)

width of rectangle. in [0, 2147483647]

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

r = 2 (float)

sphere radius. in [0, 3.40282e+38]

square: square shape mask creator, supported parameters are:

fill = 1 (bool)

create a filled shape.

width = 2 (int)

width of rectangle. in [0, 2147483647]

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. in [-3.40282e+38, 3.40282e+38]

seed = 0 (uint)

set random seed (0=init based on system time). in [0, 4294967295]

sigma = 1 (float)

standard derivation of distribution. in [0, 3.40282e+38]

uniform: Uniform noise generator using C stdlib rand(), supported parameters are:

a = 0 (float)

lower bound if noise range. in [-3.40282e+38, 3.40282e+38]

b = 1 (float)

higher bound if noise range. in [-3.40282e+38, 3.40282e+38]

seed = 0 (uint)

set random seed (0=init based on system time). in [0, 4294967295]

EXAMPLE¶

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. The bounding box will be enlarged by 3 pixels in all directions. Store the image in OpenEXR format.

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

AUTHOR(s)¶

Gert Wollny

COPYRIGHT¶

This software is Copyright (c) 1999‐2013 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'.

17 October 2014

2.0.10

Source file:	mia-2dseriessmoothgradMAD.1.en.gz (from mia-tools 2.2.2-1+b1)
Source last updated:	2014-10-17T05:50:07Z
Converted to HTML:	2019-01-13T04:25:58Z