[SciPy-User] Fwd: one-sided gauss fit -- or: how to estimate backgound noise ?

[Sturla Molden]

> On Sat, Jul 17, 2010 at 12:07 AM, Zachary Pincus

> Zach,
> 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
> distributed.

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).

Sturla