🔇 Background noise in fluorescence microscopy images

‍What noise?

‍Background noise is an inherent feature of raw fluorescence microscopy images, contrary to the common belief that modern microscopes produce flawless visuals. This noise manifests as random interference throughout the image, which is particularly crucial to consider in colocalization studies focused on small objects.

‍

‍To conduct accurate colocalization experiments, researchers must comprehend the nature of this noise and how to effectively manage it. It is essential to recognize that noise is inevitable in fluorescence microscopy images. The following overview applies to scenarios where fluorescence microscopes are well-maintained and functioning correctly, although other factors might also contribute to noise in various situations.

‍

‍Characteristics of noise:

‍Noise in fluorescence microscopy images exhibits three key characteristics:

• Randomness: it can be understood as a random positive or negative value added to the pixel value.
• Independence: the noise at each pixel is added independently of its location.
• Specific distribution: noise can be viewed as a random variable drawn from a specific distribution.

‍

‍Types of noise:

‍Fluorescence images are primarily affected by two main types of noise:

• Photon noise: this type of noise is dependent on the signal and varies across the image. It arises from the emission and detection of light and follows a Poisson distribution, where the standard deviation fluctuates with local image brightness.
• Read noise: this noise is independent of the signal and is influenced by the detector. It occurs due to inaccuracies in quantifying detected photons and follows a Gaussian distribution, with a consistent standard deviation throughout the image.

‍

‍The resulting noise in an image combines both types—photon and read noise. The actual pixel value is the sum of these two independent noise types along with the true (noise-free) rate of photon emission.

‍

‍Methods to reduce noise level:

‍

‍During image acquisition:

‍- Increase the number of detected photons by capturing images more slowly, if feasible. A greater photon count can help mitigate both types of noise. If a slower acquisition is not possible, consider taking multiple images quickly and averaging them, particularly for fixed (static) tissue.

‍

‍After image acquisition:

‍- Use background correction to reduce noise in the finalized images before performing coefficient calculations. Smart Background  Correction is an effective method for minimizing background noise because it accounts for the specific noise distribution.

‍

‍Conclusion

‍Addressing noise is essential when quantifying colocalization in fluorescence images. The most effective strategy is to minimize noise by taking the time to acquire images carefully. Additionally, applying background correction to the images can enhance the reliability of your calculations.

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍

‍