🚀 go-pugleaf

Hi Jeff:

In article <32F8D5BD.623D@bmrl.med.uiuc.edu>,
Jeffrey Tsao  <jtsao@bmrl.med.uiuc.edu> wrote:
:Hi Lester,
:...
:CRANIAL SEGMENTATION:
:If the scalp is very different in intensity from the brain, then
:a simple thresholding operation will do. Otherwise, existing methods
:require some "hacks" to combine morphological operations (e.g. dilution,
:erosion, etc.) with thresholding to gradually remove the scalp.
:You can't even do the segmentation by overlaying a model (a.k.a. 
:a template) on your images to tell you where to segment because
:*REMEMBER* you haven't figured how the brain is oriented in 3D
:(6 deg. of freedom) yet!!! This in turns leads back to --->
:Image registration in 6 deg. of freedom. DA DA!!! Quite frustrating,
:really.
:
:If anyone has some robust solutions to any part of this vicious cycle,
:I would really like to hear about them.

This is not exactly a solution, but complementary imaging processes
can offer some better resolution of some of these problems, some
as you mentioned.  The additional use of EEG is an example.  If
you require source localization, there two are two popular techniques
which overlap some in their spatial-frequency regions of best
resolution.  One method is to perform head-shape model calculations
(sometimes other imaging data is used to generate the head model),
getting an inverse (non-unique) solution to match up with calculated
electric potentials.  The hardest part of this calc is that the
skull conductivities are not known very well.  The other technique
is to perform Laplacians of data from closely nested electrodes;
Maxwells' equations give you the source currents.

The paper I mentioned previously,
         %A L. Ingber
          %T Statistical mechanics of neocortical interactions: Canonical
             momenta indicators of electroencephalography
          %J Physical Review E
          %P (to be published)
          %D 1997
          %O URL http://www.ingber.com/smni97_cmi.ps.Z
illustrates how a robust nonlinear stochastic model of minicolumnar
interactions can be developed to give "intuitive" graphical
indicators, canonical momenta indicators (CMI), which provide a
complementary method of disentangling long-fiber and short-fiber
interactions, immediately providing a natural coordinate system
that gives signal to noise enhancement over the raw data, and that
can be used further to increase resolution of sources.

Lester
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