Determination of Signal to Noise Ratio

This section offers guidelines on how to measure the Signal to Noise Ratio (SNR) in CODEX fluorescence microscopy images.

General Considerations

The SNR quantifies the amount of meaningful information that can be extracted from a physical measurement, in this case represented by fluorescence images. There are several mathematical definitions of SNR, in CODEX imaging we calculate the SNR according to the following equation:

where <I Signal> represents the mean pixel intensity of the antibody staining and <I Background> is the mean pixel intensity in tissue regions where the targeted biomarker is absent.

The SNR is an important quantification of the quality of collected images: the higher the SNR, the better is the distinction between actual fluorescence signal from antibody staining and the unwanted noise present in the image. The SNR and the Dynamic Range (DR) of an image are strongly related to each other, therefore it is important that 16bit images are acquired in CODEX experiments.


The SNR can be determined using FIJI and following a few simple steps. In this example we show how to calculate the SNR using line-scans, however SNRs can also been determined in entire regions of interest (ROIs) or in the whole image area.

For 16-bit images, two thresholds are established by the user based on visual confirmation of signal and noise localization on a 0 - 65535 scale. The mean gray value is computed for both the signal and noise thresholds, and the SNR is computed as: mean gray value of signal divided by mean gray value of noise.


  • Single channel 16bit TIFF Image at the best focus Z plane

  • FIJI (ImageJ) Software


  1. Open the TIFF image with FIJI.

  2. Ensure that selected images are not saturated as the SNR does not account for saturation effects. Saturated images should be discarded and re-acquired in different conditions.

  3. Select a Region of the tissue containing positive staining for the marker of choice

  4. Draw a line on that region covering the areas with positive staining (see Figure 1 as an example).

  5. Press Ctrl K to obtain the intensity profile for the pixels located underneath the yellow line.

  6. Save the obtained values as Signal Intensity Profile pressing the Save button underneath the graph, this will generate a csv file, that can be opened with Microsoft Excel or another preferred software.

  7. Select an ROI containing the tissue in absence of positive staining from the marker under evaluation and draw a line (see Figure 1 as an example).

  8. Press Ctrl K and obtain the corresponding intensity profile.

  9. Save the obtained values pressing the Save button underneath the graph as Noise Intensity Profile, this will generate another csv file.

  10. Using the software of choice, open the two csv files.

  11. Extract the local maximum values from the Signal Intensity profile file and calculate their average. • Average all intensity values reported in the Noise Intensity Profile file.

  12. Divide the average signal by the average noise to obtain the SNR.

  13. This procedure can be repeated in multiple regions for more accurate results.

Figure 1. FIJI linescans and corresponding intensity profiles drawn in a region of the tissue containing positive antibody staining (on the left) and in one where the targeted biomarker is absent (on the right).


What is the Dynamic Range of an image?

The Dynamic Range of a digital image is the difference between the highest and the lowest value that the pixel intensity can assume.

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