Correcting Inconsistent Illumination
There are two approaches discussed in this article to correct uneven illumination gradients: prospective and retrospective. Let’s take a closer look.
Prospective Methods
Methods that build corrective functions from reference images are prospective. The authors mention caveats due to incorrect assumptions leading to incomplete corrections of uneven illumination.
Reference images are suppose to capture all noise inherent to a system and surroundings; plus any background fluorescence (due to mounting media or specialized substrates). One of the referenced articles recommends capturing reference images across a range of exposure times to create a linear curve for each pixel. Then the noise at each pixel can be subtracted based on the exposure used to capture sample image. Unfortunately, I cannot explore using prospective methods, because I no longer have access to the microscopes used to collect the images I am processing.
Retrospective Single- and Multi-image Methods
Retrospective approaches use only images from the actual experiment. In other words, retrospective methods correct uneven illumination without the use of cell/sample free microscope images. Retrospective methods computationally smooth out intensity gradients.
Open-source tools that apply corrections to single images, like ImageJ’s Rolling Ball Correction, are effective. However, when considering a set of images in an experiment, illumination correction per image may not be sufficient, because the correction does not take into consideration all intensities in a set of images. Therefore, the relative intensity of labeled cells across a set of images may be incorrectly changed.
Multi-image methods consider groups of images when correcting illumination so that the range of intensities are relatively maintained in these groups. Groups are created based on how cells were immunocytochemicaly labeled and imaged. Let’s use Cell Profiler for applying multiple-image illumination correction.
Illumination Correction with Cell Profiler
On GNU/Linux OS setup Cell Profiler following these steps with Anaconda. The fastest way to start is importing the illumination correction pipeline into Cell Profiler and process your images. The authors of the illumination correction pipeline, described the details of their methodology here.
Select how the illumination function is calculated: “Background” or “Regular”. According to the documentation “Background” is applicable to images with few cells/objects of interest or where few cells are labeled; and “Regular” is applicable when cells cover the entire field of view. For more details and guidance please see the illumination correction pipeline documentation.
The following are my examples of the illumination correction process. In each group of three images below are my original image, the illumination function, and the processed image with corrected illumination. As you can see the results are impressive. The illumination gradients were corrected and the dynamic range increased due to a reduction in the background intensity. What I find most impressive was the removal of an artifact in the third image! In comparison, changing the brightness, contrast, and applying a threshold manually would not be as effective. The advantage of using software that creates a robust illumination correction function is evident in these examples.
