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Subject: Find gaussian humps in a 3D dataset
Date: Mon, 15 Sep 2008 11:18:02 +0000 (UTC)
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Hi all,

I have a 3D image (scalar intensity field) of particles in a fluid flow. 

There may be many particles (1<N<1000), in a large domain (up to 400^3 voxels)

Each particle could be approximately represented by a gaussian hump in intensity, surrounding the centre position. Standard deviation of the gaussian might be ~ 2 voxels.

So the question is, how best to find the positions of the particles? 

I suspect that an optimisation would be very computationally intensive. I can find local maxima very quickly to the accuracy of a single voxel; but I need to get sub-voxel accuracy - perhaps using some kind of polynomial fitting then a derivative-based approach.

Has anyone got any ideas?

Thanks,

Tom Clark