mia-2dsegment-fuzzyw(1) Run a fuzzy c-means segmentation of a 2D image.

SYNOPSIS

mia-2dsegment-fuzzyw -i <in-file> [options]

DESCRIPTION

mia-2dsegment-fuzzyw This program is a implementation of a fuzzy c-means segmentation algorithm

OPTIONS

File I/O

-i --in-file=(input, required); io
image to be segmented For supported file types see PLUGINS:2dimage/io
-c --cls-file=(output); io
class probability images, the image type must support multiple images and floating point values For supported file types see PLUGINS:2dimage/io
-o --out-file=(output); io
B-field corrected image For supported file types see PLUGINS:2dimage/io
-g --gain-log-file=(output); io
Logarithmic gain field, the image type must support floating point values For supported file types see PLUGINS:2dimage/io

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).

Segmentation parameters

-n --no-of-classes=3
number of classes to segmentnumber of classes to segment
-C --class-centres=
initial class centers
-N --neighborhood=shmean:shape=8n
neighborhood filter for B-field correctionneighborhood filter for B-field correction For supported plugins see PLUGINS:2dimage/filter
-a --alpha=0.7
weight of neighborhood filter for B-field correctionweight of neighborhood filter for B-field correction
-p --fuzziness=2

 parameter describing the fuzzyness of mattar distinction parameter describing the fuzzyness of mattar distinction 
-e --epsilon=0.01
Stopping criterion for class center estimation.Stopping criterion for class center 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.

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.

median
2D image median filter, supported parameters are:

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

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.

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

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: [email protected]

 
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: [email protected]

 
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

Run a 5-class segmentation over inpt image input.v and store the class probability images in cls.v.
mia-2dsegment-fuzzyw -i input.v -a 5 -o cls.v

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'.