[Insight-users] itkExpectationMaximizationMixtureModel
Jisung Kim
bahrahm@yahoo.com
Mon, 28 Oct 2002 06:41:07 -0800 (PST)
Hi ahmad,
--- ahmad bukhari <highway_111@mail.com> wrote:
> hi jisung and others...
> The assignment of weightages is global and hence it
> shall not be effective
> ..In a certain intensity zone a class may not be
> present at all and then
> assigning weightages does not help in any way....
No, it wouldn't be helpful. However, if a component is
correctly implemented, then it wouldn't hurt the
result neither, because the components' Evaluate
method should return very very small density value in
that intensity zone.
> An example of adding a component shall be useful for
> me.
> thanks
> ahmad
> ----- Original Message -----
> From: Jisung Kim <bahrahm@yahoo.com>
> Date: Fri, 25 Oct 2002 07:11:31 -0700 (PDT)
> To: ahmad bukhari <highway_111@mail.com>, insight
> <insight-users@public.kitware.com>
> Subject: Re: [Insight-users]
> itkExpectationMaximizationMixtureModel
>
>
> > 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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> --
>
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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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