mia-2dimagestack-cmeans(1) Calculate the c-means classification for a series of images.

SYNOPSIS

mia-2dimagestack-cmeans -i <in-file> -o <out-probmap> [options]

DESCRIPTION

mia-2dimagestack-cmeans This program first evaluates a sparse histogram of an input image series, then runs a c-means classification over the histogram and then writes the probability mapping for thr original intensity values

OPTIONS

File-IO

-i --in-file=(input, required); io
input image(s) to be filtered For supported file types see PLUGINS:2dimage/io
-o --out-probmap=(output, required); string
Save probability map to this file

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

Parameters

-T --histogram-thresh=5; float in [0, 50]
Percent of the extrem parts of the histogram to be collapsed into the respective last histogram bin.
-C --classes=kmeans:nc=3
C-means class initializerC-means class initializer For supported plugins see PLUGINS:1d/cmeans

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/cmeans

even
C-Means initializer that sets the initial class centers as evenly distributed over [0,1], supported parameters are:

nc =(required, ulong)
Number of classes to use for the fuzzy-cmeans classification.

kmeans
C-Means initializer that sets the initial class centers by using a k-means classification, supported parameters are:

nc =(required, ulong)
Number of classes to use for the fuzzy-cmeans classification.

predefined
C-Means initializer that sets pre-defined values for the initial class centers, supported parameters are:

cc =(required, vdouble)
Initial class centers fuzzy-cmeans classification (normalized to range [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: [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

 

EXAMPLE

Run the program over images imageXXXX.png with the sparse histogram, threshold the lower 5% bins (if available), run cmeans with three classes on the non-zero pixels.
mia-2dimagestack-cmeans -i image0000.png -o cmeans,txt --histogram-tresh=5 --classes 3

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'.