[SciPy-User] Fwd: one-sided gauss fit -- or: how to estimate backgound noise ?
Tue Jul 20 16:03:02 CDT 2010
On Tue, Jul 20, 2010 at 10:38 PM, Sturla Molden <firstname.lastname@example.org> wrote:
>> On Sat, Jul 17, 2010 at 12:07 AM, Zachary Pincus
>> thanks for your reply. The idea is to calculate the mean/std of the
>> *background noise* of the underlying (2d or 3d) image data based on
>> the image's 1d image intensity histogram.
>> Regarding the "tail", the problem is that in general the signal
>> intensities are not well separated from the background. Thus, the
>> right half of the background's (Gaussian) noise distribution may
>> already be significantly miss-shaped - whereas the left side, i.e. all
>> values below the mean background level, should be nicely Bell-shape
> Have you considered fitting a mixture model using the EM algortithm? You
> could e.g. include one Gaussian for the signal and a different probability
> model for the noise (e.g. a Poisson process). Start by fitting a Gaussian
> the standard way (maximum likelihood), then use the EM to prune out the
> noise samples. you can then see this as a data clustering problem (i.e.
> you try to classify each point as being signal or noise).
As far as I know I can only expect Gaussian distribution for the noise
of the background. The (foreground) signal could in general be of any
kind - including few well separated events, or a broad intensity
distribution just about background mean, or something in between ...
More information about the SciPy-User