[Insight-users] Re: itkExpectationMaximizationMixtureModelEstimator

[Luis Ibanez]

Hi digvijay,

I may be wrong in my interpretation...
(Jisung, please correct me if this is not
the right description)

--

The MixtureModelExtimator takes a population
of samples and classify them in groups
(classes).  The samples will be partitioned
by assigning each one of them to one specific
class.

The array of Proportions indicates the
percent of samples belonging to each class.

Let's say that you are classifying brain
tissues into

     1 - white matter
     2 - gray matter
     3 - CSF

You will have three classes, plus one for
rejection. If your image has N pixels and
you anticipate that:

     n1 pixel will belong to white matter
     n2 pixel will belong to gray matter
     n3 pixel will belong to CSF
     n4 pixel are in a rejection class
              (e.g. bone, skin, background )

with     N = n1 + n2 + n3 + n4

This is apriori knowledge based on a typical
brain.

In your case, for the Histogram you can
assume that the gray level distribution
of each tissue is a gaussian. The total
histogram is then the composition of four
gaussians.

You can load the initial proportions array
(which is of size = 4) with the values:

    {  n1/N ,  n2/N , n3/N , n4/N }


Then, run the estimator. When the estimator
is done the samples are now classified into
the four groups and new final proportions
are computed by the estimator.

The TrueProportions array (size=4) will contain
the final percents of samples (pixels in this
case) belonging to each class.

Note that the Estimator let you select the
type of distribution for each class. This is
done with the ComponentType. In the case of
Gaussian distributions you may use:

itk::stat::GaussianMixtureModelComponent< DataSampleType >

-----

You may see an example of this class at

      Insight/Testing/Code/Numerics/Statistics/

itkExpectationMaximizationMixtureModelEstimatorTest.cxx


Please let us know if you have further questions.


Thanks


     Luis


==================================================

digvijay singh wrote:
  >
 > hi luis !!
 > i had a look at the
 > itkExpectationMaximizationMixtureModelEstimator
 > could you please give me some more info on the use of
 > trueproportions[] and the initialproportions[].
 > thanks
 > digvijay
 >
 >
 >
 >