Fluorescence Images
content that is likely to be caused by out-of-focus blur. Tis removal can also be preceded by a nearest-neigh- bors deblurring to eliminate even more “blurry” structures. Figure 6 shows fluorescently labeled nuclei acquired with an inverted WF setup. Te original WF image has then been processed to clear the haze from the image by either a background sub- traction based on the rolling ball algorithm (as implemented in ZEISS ZEN imaging soſtware) or by a series of image processing steps available in the Adobe Lightroom® photo process- ing soſtware. As seen in Figures 7 and 8, sim-
Figure 7: Single-plane and maximum-intensity (ortho) projections of a WF image of a polychaete worm are stained in green and red (left). The image stack was processed with a rolling ball background subtraction in ZEISS ZEN lite, and the result is shown in the middle. Compared to the WF image, more structures are visible in the back- ground subtracted dataset, but many details are missing in relation to the images acquired by optical sectioning with an Apotome.2 (right). In addition, some of the compact green structure on the edges seems to have eroded. Image stack height: 160 μm, 400 planes.
Figure 8: 3D rendering of a z-stack of a zebrafish, stained in blue, green, and red (left). The image stack was pro- cessed with a rolling ball background subtraction in ZEISS ZEN lite, and the result is shown in the middle. Com- pared to the WF image, more structures are visible in the background subtracted dataset, but many of the details are missing in relation to the images acquired with the Apotome.2 (right). Image stack height: 120 μm, 80 planes.
ple background subtraction leads to removal of the haze in the images but does not reveal all fine details in them. Also, the resolution of the image is not enhanced by the background subtrac- tion. Tis becomes especially apparent when the same image is not only com- pared to a WF image, but also to one that has been acquired or processed with more advanced techniques, such as 3D deconvolution and/or opti- cal sectioning methods. One further problem that is sometimes seen with background subtraction, especially with the rolling ball method, is an overrepresentation of faint spots in the resulting image, as well as box- like artifacts. To reduce this, several modifications to the rolling ball algo- rithm have been made; for example, the floating ball (Bio-Rad) method is a modification of a combination of fuzzy and rough set theories [5]. In addition, many efforts have been made to subsequently remove arti- facts from images that are caused by rolling ball and lowpass filtering [6]. No matter how much these tech-
Figure 9: Giant liver fluke stained with Hoechst 33342. The homogeneous fluorescence in the inner parts of the WF image (left) poses a serious problem for background correction algorithms (center). Some structures remain, but generally there are too many black spaces between the cells. This becomes visible when comparing the results to an optical section, acquired with ZEISS Apotome.2 (right). Notably, the prominent rim around the structure, as seen by the background corrected image in the center panel, is an artifact of interference in the WF image, which is not seen with an optical sectioning system.
Microsystems marketed as “Tunder™ Imager”, is based on the principle of separating out the background and subtracting it from the image. No-neighbor and rolling-ball methods, as well as ICC, employ filter algorithms to identify and remove image
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niques are improved, their general hallmark and working principle is signal exclusion, essentially based on feature size. Tis can lead to arti- fact generation and loss of informa- tion. A good example of lost signal is the “hole” in the inner parts of larger
organisms or spheroids that is generated by background correc- tion or computational clearing. Te homogeneous fluorescence in the inner part has properties similar to the homogeneous blur of out-of-focus light, and it is therefore “removed” (Figure 9).
www.microscopy-today.com • 2020 November
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