Bleaching Correction

General information

Photobleaching is a common problem in fluorescence microscopy. It leads to a gradual decrease in fluorescence signal intensity over time and results in intensity decay, loss of information and - if not corrected - to false interpretations. Therefore it is crucial to compensate for signal intensity loss. There are several Bleaching correction approaches available.

Offered algorithms

In the framework of this project, we work on Calcium Microdomains (see reference paper: Diercks et al. - 2019 - High-resolution calcium imaging method for local calcium signaling). This program was developed to automatically process calcium imaging data. Similar to the reference paper, we carry out an additive frame-by-frame bleaching correction algorithm. Local calcium imaging employs the two dyes Fura-Red and Fluo-4. Given the negligible bleaching of Fluo-4 in comparison to Fura-Red, only the bleaching characteristics of Fura-Red are considered. Alternatively, we offer two more bleaching correction algorithms: simple multiplicative correction (based on the ratio of Frame 0 and Frame i) and a bi-exponential fit with additive correction (using a curve-fit-function from scipy).

Bleaching correction Algorithm (additive frame-by-frame, no fit): Pseudocode

Here is an exemplified code of the bleaching correction:

# ref_i: reference image series (Fura_Red)
reference_mean_intensity = measure_mean_intensity(ref_i[frame_0])
for frame_n in ref_i:
    mean_intensity = measure_mean_intensity(ref_i[frame])
    difference = reference_mean_intensity - mean_intensity
    for pixel in ref_i[frame_n]:
        if pixel_value > 0:
            pixel_intensity += difference

Example of a bleaching corrected intensity timeline:

Blue = correction
Red = without correction