<?xml version="1.0" encoding="utf-8"?>
<executable>
<category>
CMTK.Miscellaneous
</category>
<title>
Gaussian mixture model segmentation
</title>
<description>
Segment an image into c classes using the EM algorithm for Gaussian mixtures with optional priors.
</description>
<license>
http://www.fsf.org/licensing/licenses/gpl.html
</license>
<contributor>
Torsten Rohlfing, with contributions from Michael P. Hasak, Greg Jefferis, Calvin R. Maurer, and Daniel B. Russakoff
</contributor>
<acknowledgements>
CMTK is supported by the National Institute of Biomedical Imaging and BioEngineering under Grant EB008381
</acknowledgements>
<documentation-url>
https://neuro.sri.com/cmtk/wiki/
</documentation-url>
<version>
</version>
<parameters>
<label>
General
</label>
<description>
General Classification Parameters
</description>
<string>
<channel>
input
</channel>
<default>

</default>
<description>
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.
</description>
<name>
mask
</name>
<label>
mask
</label>
<flag>
-m
</flag>
<longflag>
--mask
</longflag>

</string>
<integer>
<default>
3
</default>
<description>
Number of classes.
</description>
<name>
classes
</name>
<label>
classes
</label>
<flag>
-c
</flag>
<longflag>
--classes
</longflag>

</integer>
<integer>
<default>
10
</default>
<description>
Number of EM iterations.
</description>
<name>
iterations
</name>
<label>
iterations
</label>
<flag>
-n
</flag>
<longflag>
--iterations
</longflag>

</integer>

</parameters>
<parameters>
<label>
Priors
</label>
<description>
Handling of Priors
</description>
<boolean>
<description>
Use priors for initialization only.
</description>
<name>
priors_init_only
</name>
<label>
priors_init_only
</label>
<longflag>
--priors_init_only
</longflag>

</boolean>
<double>
<default>
0
</default>
<description>
Small value to add to all class priors to eliminate zero priors.
</description>
<name>
prior_epsilon
</name>
<label>
prior_epsilon
</label>
<flag>
-e
</flag>
<longflag>
--prior_epsilon
</longflag>

</double>

</parameters>
<parameters>
<label>
Output
</label>
<description>
Output Parameters
</description>
<boolean>
<description>
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).
</description>
<name>
probability_maps
</name>
<label>
probability_maps
</label>
<flag>
-p
</flag>
<longflag>
--probability_maps
</longflag>

</boolean>

</parameters>

</executable>

