Fluorescence Images
Table 1: Factors that must be considered when collecting 3D volume data and using deconvolution. Super-
SIM Performance for: Out-of-Focus Discrimination
Thin Sample Lateral Resolution Thin Sample Axial Resolution Thick Sample Lateral Resolution Thick Sample Axial Resolution Acquisition Speed Simple Operation
DCV •••
Simultaneous Multichannel Acquisition •• Depth of Penetration
•
••••• ••••• ••• •
•••••• ••••
Apotome.2 CLSM Airyscan ••• •• ••
••••••
••••• •••• ••• ••• ••
••••
•••• ••••• ••
Light Sheet
••• ••
•••••• ••••• ••••• •••• ••••• •••• •••••
• •
••••• •••••• •••• ••••• •••••• ••••• ••• •••
•••
•••• •••••• •••
resolution PALM
•••••• •
•••••• ••••••
• •
Super-
resolution SIM
••• •• •
•••••
•••••• ••• ••• •• ••
Spinning Disk
•• •• ••
••• ••• ••• •••
••••• •••
positive side, both methods require very little computing power and are fast (vir- tually executed in real-time), but they are also very limited in their capabilities and oſten mishandle blurred light. Tey also reduce signal intensities (usually around 50 to 90%) across the image and should only be used for a quick check of the image when, for example, further optimization of the image acquisition parameters is necessary.
Figure 5: Unprocessed WF image of a cell (left) and the same image processed with unsharp masking (right). Actin filaments in green, mitochondria in purple.
Background Subtraction Tis is a very effective way to
Figure 6: Top row: Grayscale images of fluorescently labeled nuclei. Out-of-focus blur is visible in the original WF image (left), as there are multiple layers of nuclei above and below the focus plane. Rolling ball background subtraction was carried out with the original WF image in ZEISS ZEN lite (middle), eliminating most of the haze in the image. A series of adjustments (90% dehaze, 80% clarity, 80% texture, 80% blacks, 10% contrast) to the original WF image have been made with the software Adobe Lightroom® (right), resulting in a very similar elimination of haze, as compared to the background subtracted image in the middle. Bottom row: As a gold standard, the same image plane with a maximum intensity projection is shown, based on a stack of optical sections acquired with a ZEISS Apotome.2.
2020 November •
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increase contrast in WF images, offering many possibilities for improving a WF image by using image and computational processing to reduce the background. One method that has been around for a very long time is the so-called rolling ball background subtraction algorithm. Tis was inspired by Stanley Sternberg’s arti- cle, “Biomedical Image Processing” in IEEE Computer [4]. It removes continu- ous or uneven background from images by determining a local background value for every pixel, averaged over a very large ball around the pixel. Te value is there- aſter subtracted from the original image. Tus, the most important parameter is the rolling ball radius, which should be at least as large as the radius of the largest object that is not part of the background. It is quite easy to estimate the parameter when the magnification of the system is known, but it can never be accurate when the sample types change or the structures in them are uncommon. Te so-called instant computational clear- ing (ICC) used in products of Leica
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