Eastimation signal to noise ratio on imageJ

[thpnick688]

Hi, everyone,
     I have encountered a question on estimating signal to noise ratio on
imageJ. I have an image with 4x4 binning. The binning is done by the CCD
detector not by the software. I use the ROI to measure the mean gray level
intensity on the main object in the image. I also use the same size ROI on
the background to measure the background noise. I use the simple equation
"S/N= “mean gray level intensity in subject” / “mean gray level intensity of
the noise”" to estimate the signal to noise ratio. However, by comparing
with an image that is unbinned, I didn't see the SNR is improved after the
calculation. So, what mistakes did I make?

RL



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[anusuya pal]

Hello,

May I know what do u mean by binning? Is the whole image is divided into
16segments (4x4) ? And then, all the 16 segments are stitched to get the
whole image ( which you are calling unbinned)?

Thanks
Anu

On 22-Oct-2017 11:29 AM, "thpnick688" <[hidden email]> wrote:

> Hi, everyone,
>      I have encountered a question on estimating signal to noise ratio on
> imageJ. I have an image with 4x4 binning. The binning is done by the CCD
> detector not by the software. I use the ROI to measure the mean gray level
> intensity on the main object in the image. I also use the same size ROI on
> the background to measure the background noise. I use the simple equation
> "S/N= “mean gray level intensity in subject” / “mean gray level intensity
> of
> the noise”" to estimate the signal to noise ratio. However, by comparing
> with an image that is unbinned, I didn't see the SNR is improved after the
> calculation. So, what mistakes did I make?
>
> RL
>
>
>
> --
> Sent from: http://imagej.1557.x6.nabble.com/
>
> --
> ImageJ mailing list: http://imagej.nih.gov/ij/list.html
>

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[thpnick688]

Hi, When I mean "binning" is the process of combining or pooling together of
photoelectrons of adjacent
pixels on the CCD to form electronic superpixels. Pooling of electrons
occurs in the serial register of the CCD during readout. "unbinned" means
the binning factor is 1 which is the default.



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[Herbie]

Dear,

mean values don't help for the evaluation of the S/N-ratio. You need at
least the alternating powers.

But things aren't as easy as one may think and one should consider that
noise may be non-additive. Furthermore, it may be helpful to know the
Fourier-spectral dependence of the S/N-ratio.

For a 2x2 binning one could in theory expect a reduction of uncorrelated
(photon) noise by a factor of two.

Just some thoughts

Herbie

::::::::::::::::::::::::::::::::::::::::
Am 22.10.17 um 17:18 schrieb thpnick688:

> Hi, everyone,
>       I have encountered a question on estimating signal to noise ratio on
> imageJ. I have an image with 4x4 binning. The binning is done by the CCD
> detector not by the software. I use the ROI to measure the mean gray level
> intensity on the main object in the image. I also use the same size ROI on
> the background to measure the background noise. I use the simple equation
> "S/N= “mean gray level intensity in subject” / “mean gray level intensity of
> the noise”" to estimate the signal to noise ratio. However, by comparing
> with an image that is unbinned, I didn't see the SNR is improved after the
> calculation. So, what mistakes did I make?
>
> RL
>
>
>
> --
> Sent from: http://imagej.1557.x6.nabble.com/
>
> --
> ImageJ mailing list: http://imagej.nih.gov/ij/list.html
>

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[Jeremy Adler-2]

I have always been a little suspicious of signal to noise  - it clearly works for simple signals like morse code, where it is very clear which is the signal and which is noise, and importantly the signal is nominally constant (noise aside).
In most images the signal is variable across the image so (a) there is usually an overlap between the high end of the noise range and the low end of the signal + noise and also a set of edge pixels that include a mix of background and foreground , (b) the mean, while useful, can be deceptive especially if the noise comes from variation in the number of photons - a particular problem in confocal microscopy with a standard deviation being the square root of the longterm mean number of photons for each pixel - it therefore varies from pixel to pixel.

An alternative is to compare two replicate images, which are nominally identical (if the specimen is static) but differ due to noise.  This can be displayed using a scatterplot - something it would be great to see in acquisition software to give a clear visual indication noise. We have used the correlation between replicate images as a measure of noise then used this to  then correct colocalization measurements between confocal fluorescent images made by correlation.

Replicate-based noise corrected correlation for accurate measurements of colocalization
Journal of Microscopy
Volume 230, Issue 1, April 2008, Pages: 121–133, J. ADLER, S. N. PAGAKIS and I. PARMRYD

For binning - you can bin your own image
Image / Transform / Bin

Jeremy Adler



-----Original Message-----
From: ImageJ Interest Group [mailto:[hidden email]] On Behalf Of thpnick688
Sent: 22 October 2017 17:18
To: [hidden email]
Subject: Eastimation signal to noise ratio on imageJ

Hi, everyone,
     I have encountered a question on estimating signal to noise ratio on imageJ. I have an image with 4x4 binning. The binning is done by the CCD detector not by the software. I use the ROI to measure the mean gray level intensity on the main object in the image. I also use the same size ROI on the background to measure the background noise. I use the simple equation "S/N= “mean gray level intensity in subject” / “mean gray level intensity of the noise”" to estimate the signal to noise ratio. However, by comparing with an image that is unbinned, I didn't see the SNR is improved after the calculation. So, what mistakes did I make?

RL



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[Michael Schmid]

Hi RL,

the background level and noise are two different quantities.
Noise means fluctuations, i.e. signal components that are different from
image to image.

To measure the dark and readout noise, take two dark frames (i.e. images
with the camera shielded from light), subtract them (Image Calculator
with '32-bit' result), measure the standard deviation and divide by sqrt(2).
If you vary the exposure time, you can also differentiate dark current
noise and readout noise (in the limit of zero exposure time, you have
only the readout noise).

You can also measure the noise under illumination conditions: take two
images with *exactly* the same camera position and illumination,
subtract them (make sure that the result contains no image information
any more!). Again, take the standard deviation of the result and divide
by sqrt(2). This noise includes the shot noise of the photons.


Michael
________________________________________________________________
On 22/10/2017 17:18, thpnick688 wrote:

> Hi, everyone,
>       I have encountered a question on estimating signal to noise ratio on
> imageJ. I have an image with 4x4 binning. The binning is done by the CCD
> detector not by the software. I use the ROI to measure the mean gray level
> intensity on the main object in the image. I also use the same size ROI on
> the background to measure the background noise. I use the simple equation
> "S/N= “mean gray level intensity in subject” / “mean gray level intensity of
> the noise”" to estimate the signal to noise ratio. However, by comparing
> with an image that is unbinned, I didn't see the SNR is improved after the
> calculation. So, what mistakes did I make?
>
> RL
>
>
>
> --
> Sent from: http://imagej.1557.x6.nabble.com/
>
> --
> ImageJ mailing list: http://imagej.nih.gov/ij/list.html
>

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