[Insight-developers] Memory efficient gaussian mixture models : ITK Statistics Refactoring

[Luis Ibanez]

Hi Richard,


               Thanks for this suggestion.


As you probably know, we are in the process of refactoring the
ITK Statistics framework.

We have not reworked the classifiers themselves yet,
and that may have to wait for several months.

However, we will take into account your suggestion once we
get there.

In general:

     the Histogram should be a valid input,
     to any filter that expects a Sample as input.


For a description of the ongoing Statistics Framework refactoring
please look at:

http://www.itk.org/Wiki/Proposals:Refactoring_Statistics_Framework_2007

The review of the current statistics framework,
please look at:

http://www.itk.org/Wiki/Proposals:Refactoring_Statistics_Framework_2007_Class_Manifesto


For instruction on how to migrate from the current framework
to the new framework, please look at:

http://www.itk.org/Wiki/Proposals:Refactoring_Statistics_Framework_2007_Migration_Users_Guide


For a description of the new framework, please look at:
http://www.itk.org/Wiki/Proposals:Refactoring_Statistics_Framework_2007_New_Statistics_Framework



Please let us know if you have further suggestions,


     Thanks



        Luis


--------------------
Richard Beare wrote:
> Hi,
> 
> Is there a simple way of adapting
> ExpectationMaximizationMixtureModelEstimator so that histogram
> versions of the samples can be used? I'm hoping to compute parameters
> of mixture models for a reasonable number of MR scans combined, and
> adapting the current implementation to a histogram or map based
> approach seems an obvious way to go. This will probably require a new
> version of  itkGaussianMixtureModelComponent.h and
> itkWeightedMeanCalculator etc, but I was wondering whether there are
> any short cuts I might have missed.
> 
> Thanks
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