Contrary to popular belief that modern microscopes provide smooth and flawless picture, raw fluorescence microscopy images are actually ALWAYS degraded by noise. This noise appears as random background “crumbliness” throughout the image. Since most colocalization studies focus on tiny objects in the images, this background noise simply cannot be ignored.
To properly execute colocalization experiments, researchers should understand the nature of this noise and know what they can do to address it appropriately. You need to realize that the noise is INEVITABLE and its description below applies to cases when fluorescence microscope are well maintained and properly functioning. In other cases, additional factors may also apply.
Noise in fluorescence microscopy images has 3 important characteristics:
Fluorescence images are impacted by 2 major types of noise:
Type 1. Photon noise. Signal-dependent, varies throughout the image. Comes from the emission and detection of the light. Follows a Poisson distribution, in which the standard deviation changes with the local image brightness.
Type 2. Read noise. Signal-independent, depends on the detector. Comes from inaccuracies in quantifying numbers of detected photons. Follows a Gaussian distribution, in which the standard deviation stays the same throughout the image.
Increase the number of detected photons by acquiring images more slowly, if possible. Detecting more photons helps to overcome both types of noise. If this is not an option and in the case of fixed (static) tissue, you may consider recording multiple images quickly and then averaging them.
When images have been acquired and the level of noise in them is finalized, prior to performing coefficients calculations this noise should be removed using procedure called background correction. Smart Background Correction is a particularly efficient way to remove background noise because it considers the especially complex property of noise that follows specific distribution.
When quantifying colocalization in fluorescence images, you will ALWAYS need to address the issue of noise. The best strategy is to minimize the amount of noise before acquiring images by taking time to acquire them slowly. After that, you will need to remove the noise present in the image by using step called background correction. Keeping this in mind will ensure reliability of your calculations.
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