[Insight-users] itkExpectationMaximizationMixtureModel

Jisung Kim bahrahm@yahoo.com
Fri, 25 Oct 2002 07:11:31 -0700 (PDT)


Hi ahmad,


--- ahmad bukhari <highway_111@mail.com> wrote:
>  hi everyone!! 
> There is another issue that i need help with: 
> the mixture model can finally give you an estimate
> of 
> the different classes presence....but the trouble is
> 
> that different classes occur in different intensity 
> regions. For example in an intensity zone on  a
> typical 
> frequency histogram plot different classes occur in 
> different regions interfering in some cases and
> isolated 
> in rest. So the mixture model perhaps should be
> applied 
> (initialized)differently for different intensity
> zones 
> to get an accurate picture. Is there a way out...?? 

Each iteration of the EM estimation process updates
weights for measurement vectors with given
"components". In other words, in the test program,
each measurement vector has two weight values that
tells you how likely the measurement vector belongs to
the two Gaussian classes. When the Gaussian components
update  their parameters, they use the weights instead
of using simple mean and covariance calculations. This
weighted mean and weighted covariance calculations
actually has similar effect that you mentioned.  If a
measurement vector highly likely belong to a component
(let's say component A) but not to  the other
component (component B), then the measurement vector
will affects the parameter calculation of component A
but won't affect much that of component B.

Is this what you need?

  

> thanks 
> ahmad   
> -- 
> 

Thanks,

Jisung.

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=====
Jisung Kim
bahrahm@yahoo.com
106 Mason Farm Rd.
129 Radiology Research Lab., CB# 7515
Univ. of North Carolina at Chapel Hill
Chapel Hill, NC 27599-7515

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