Fluorescence Images
Unsharp Masking Figure 5 shows detail from the cells seen in Figure 2 and
Figure 3: Acquisition of a WF image z-stack (left) supports processing by deconvolution to reconstruct a 3D volume as opposed to a 3D object creation with an optical sectioning approach (right).
images. Tese procedures have several primary goals, for example, to remove shading, improve contrast, remove blur, or enhance certain structures—all, of course, with the ulti- mate goal of making the image more suitable for inspection or presentation. However, before applying these methods to micrographs, first consider the impact they will have on the image itself. Many post-processing steps introduce noise (to a varying degree) to the image, and simple post-processing steps can never increase its information content. Never- theless, a processing step might be exactly what is needed to make the information and details that are present in an image more apparent, or just to make it more appealing visually. Let’s look at some of the most commonly used pro- cessing methods. Tese could be categorized as “deblurring” methods, and they all have one thing in common: unlike deconvolution, they work on one image aſter another with- out including any 3D volume information or a mathemati- cal model of the distortions generated in the instrument that acquired them.
illustrates what a relatively simple procedure called “edge detec- tion” can do to any WF fluorescence image. On the leſt is the raw, unprocessed region of interest (ROI). On the right, the same ROI is shown aſter a single processing step using the unsharp masking method, introduced by Russ in 1994 [1]. Te effect is quite impressive and, at first sight, the unprocessed image might even look a bit out of focus next to the much sharper, pro- cessed image. Tis impression is a result of humans being hard- wired to look for sharp edges and lines and to consider these to be more detail-rich than fuzzier representations of the same image. Unsharp masking is like many post-processing meth- ods in that it will not add to, but instead will reduce the infor- mation content of the image. Te loss of information is easily understood if we look at the way this sharpening is achieved. Te algorithm subtracts an unsharp mask from the specimen image. Te mask is simply an artificially blurred image that is produced by applying a Gaussian low-pass filter to the origi- nal image. Consequently, this procedure inevitably increases noise in the image. Nevertheless, edge-enhancing methods do a good job in making geometrical structures stand out more prominently, especially for thin and transparent objects like 2D cell cultures. As a free bonus, the unsharp mask filter also sup- presses low-frequency details and can be used to correct shad- ing throughout an image. Tis is oſten visible in the form of slowly varying background intensities.
No-Neighbor/Nearest-Neighbors Tese two processing methods are sometimes classified as
deconvolution methods. However, although the nearest-neigh- bors method in particular shares some characteristics with real 3D deconvolution procedures, both processing methods are fil- ters designed to subtract the estimated blur from the image. Te early concept was introduced to light microscopy by Castleman in 1979 [2] and first applied practically by Agard in 1984 [3]. Te no-neighbor method achieves deblurring by looking only at the image itself, whereas the near- est-neighbors method also takes into account information from one plane above and one below, which means a z-stack must be acquired. Te nearest-neighbors method attempts to remove the blur contribution in the center focal plane by subtracting defocused versions of slices
including knowledge of
Figure 4: Mitochondrial membranes labeled with TOMM22, acquired with a ZEISS LSM system using fast linear scanning (left). The top row shows a single plane of the dataset displayed before (left) and after the image was processed with a constrained iterative deconvolution in ZEISS ZEN imaging software, revealing additional details and increasing the resolution (right). The bottom row shows a detail of the yellow inset region and the improved image quality following deconvolution.
38
the adjacent the
point spread function (PSF), and this leaves only sharp features behind. Te no-neighbor method is similar although it only considers a single slice. It is therefore equivalent in prin- ciple to an unsharp masking, which we have already discussed. On the
www.microscopy-today.com • 2020 November
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76
