Path: news.mathworks.com!not-for-mail
From: Peter Perkins <Peter.PerkinsRemoveThis@mathworks.com>
Newsgroups: comp.soft-sys.matlab
Subject: Re: Find gaussian humps in a 3D dataset
Date: Mon, 15 Sep 2008 08:01:14 -0400
Organization: The MathWorks, Inc.
Lines: 15
Message-ID: <galiqa$6es$1@fred.mathworks.com>
References: <galg9a$995$1@fred.mathworks.com>
NNTP-Posting-Host: perkinsp.dhcp.mathworks.com
Mime-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1; format=flowed
Content-Transfer-Encoding: 7bit
X-Trace: fred.mathworks.com 1221480074 6620 172.31.57.88 (15 Sep 2008 12:01:14 GMT)
X-Complaints-To: news@mathworks.com
NNTP-Posting-Date: Mon, 15 Sep 2008 12:01:14 +0000 (UTC)
User-Agent: Thunderbird 2.0.0.16 (Windows/20080708)
In-Reply-To: <galg9a$995$1@fred.mathworks.com>
Xref: news.mathworks.com comp.soft-sys.matlab:490268



Thomas Clark wrote:
> 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? 

Tom, it's possible that the GMDISTRIBUTION function in the Statistics Toolbox, which fits a Gaussian mixture distribution, might be helpful.  I don't know enough about your application, though.

- Peter Perkins
  The MathWorks, Inc.