Run a fuzzy c-means segmentation of a 2d image.
mia-2dsegment-fuzzyw -i <in-file> [options]
mia-2dsegment-fuzzyw This program is a implementation of a fuzzy c-means segmentation algorithm
image to be segmented For supported file types see PLUGINS:2dimage/io
class probability images, the image type must support multiple images and floating point values For supported file types see PLUGINS:2dimage/io
B-field corrected image For supported file types see PLUGINS:2dimage/io
Logarithmic gain field, the image type must support floating point values For supported file types see PLUGINS:2dimage/io
verbosity of output, print messages of given level and higher priorities. Supported priorities starting at lowest level are:
info \(hy Low level messages
trace \(hy Function call trace
fail \(hy Report test failures
warning \(hy Warnings
error \(hy Report errors
debug \(hy Debug output
message \(hy Normal messages
fatal \(hy Report only fatal errors
print copyright information
print this help
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).
number of classes to segment
initial class centers
neighborhood filter for B-field correction For supported plugins see PLUGINS:2dimage/filter
weight of neighborhood filter for B-field correction
parameter describing the fuzzyness of mattar distinction
Stopping criterion for class center estimation.
gauss
spacial Gauss filter kernel, supported parameters are:
w = 1 (int)
half filter width. in [0, 2147483647]
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]
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)
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 \(hy test stopping function
tuckey \(hy tukey stopping function
pm1 \(hy stopping function 1
pm2 \(hy 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 \(hy apply a linear transformation that maps the real input range to the full output range
range \(hy apply linear transformation that maps the input data type range to the output data type range
copy \(hy copy data when converting
linear \(hy apply linear transformation x -> a*x+b
optstat \(hy 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 \(hy floating point 32 bit
sbyte \(hy signed 8 bit
double \(hy floating point 64 bit
sint \(hy signed 32 bit
ushort \(hy unsigned 16 bit
sshort \(hy signed 16 bit
uint \(hy unsigned 32 bit
bit \(hy binary data
ubyte \(hy 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 \(hy set values outside the mask to the maximum value found in the image..
zero \(hy set the values outside the mask to zero.
min \(hy 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]
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
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]
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
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]
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
Gert Wollny
This software is Copyright (c) 1999\(hy2013 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'.