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gmm(1) The Computational Morphometry Toolkit gmm(1)


gmm - Gaussian mixture model segmentation


gmm InputImage OutputImage PriorImages


Segment an image into c classes using the EM algorithm for Gaussian mixtures with optional priors.


Global Toolkit Options (these are shared by all CMTK tools)

Write list of basic command line options to standard output.
Write complete list of basic and advanced command line options to standard output.
Write list of command line options to standard output in MediaWiki markup.
Write man page source in 'nroff' markup to standard output.
Write command line syntax specification in XML markup (for Slicer integration).
Write toolkit version to standard output.
Write the current command line to standard output.
Set verbosity level.
Increment verbosity level by 1 (deprecated; supported for backward compatibility).
Set maximum number of parallel threads (for POSIX threads and OpenMP).

General Classification Parameters

Path to foreground mask image. If this is not provided, the input image is used as its own mask, but this does not work properly if the input image itself has pixels with zero or negative values. [Default: NONE]
Number of classes. [Default: 3]
Number of EM iterations. [Default: 10]

Handling of Priors

Use priors for initialization only.
Small value to add to all class priors to eliminate zero priors. [Default: 0]

Output Parameters

Write probability maps. The file names for these maps will be generated from the output image path by inserting '_prob#' before the file format suffix, where '#' is the index of the respective class, numbered starting at 1 (zero is background).


Torsten Rohlfing, with contributions from Michael P. Hasak, Greg Jefferis, Calvin R. Maurer, Daniel B. Russakoff, and Yaroslav Halchenko



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CMTK is developed with support from the NIAAA under Grant AA021697, National Consortium on Alcohol and Neurodevelopment in Adolescence (N-CANDA): Data Integration Component. From April 2009 through September 2011, CMTK development and maintenance was supported by the NIBIB under Grant EB008381.

Jul 7 2024 CMTK 3.3.1p2