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Scanning Electron Microscopy


Note: Regardless of the colorizing or 3D technique described above, all images in this article were processed using MountainsMap  soſt ware. Most SEM manufacturers provide soſt ware based on MountainsMap  with their instruments, as standard or as an option. For more details go to the following: www.digitalsurf.com .


Figure 9 : SE SEM image of Bacillus cereus, responsible for some types of food poisoning. (a) Original image from the internet. (b) Same image rendered in 3D with pseudocoloring. Image courtesy of Mogana Das Murtey and Patchamuthu Ramasamy. Licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license, from WikiMedia ( https://commons.wikimedia.org/wiki/File:Bacillus_cereus_SEM-cr.jpg ).


In Figure 8 , object-oriented segmentation was used to separate the objects in the image. T is involved a combination of high-pass fi ltering, a classical watershed algorithm to fi nd object contours, a set of post-processing rules to group or dissociate objects, and some integration to produce a height map. Altogether there are more than 30 operations, but the user is only required to indicate the average size of the objects as a setting. T e height map ( Figure 8b ) can then again be used as a distorted mesh on which the original SEM image is pasted as a texture. Once the height map is obtained, it can also be used to blend a false-color scale with the SEM image as shown in Figure 8c .


Figure 9 shows another image from the internet. Again the single-image reconstruction operator used object-oriented segmentation and 3D rendering to provide both color and shading, giving the image a pleasing and believable 3D-like presentation.


On complex images with multiple object sizes and shapes such as this one, not all objects are reconstructed perfectly in 3D; some might look “defl ated.” However, for topography coming from a single SEM image, it is the most one can do so far and allows an easy fi rst approach of the 3D surface. It can be used for instance to compare the relative surface texture properties within a batch of samples or to count objects where classic image segmentation would fail.


Conclusion T e availability of image enhancement and 3D analysis soſt ware for SEM promises to alter image processing practices in science and industry. It crosses an important threshold in allowing researchers to improve visualization and interpre-


tation of objects in the nanoworld through the addition of color and depth. From a practical point of view, the ease of operation frees up precious hours, allowing scientists to spend more time analyzing and applying results rather than producing them.


Acknowledgements T e author would like to thank the many electron micros- copists who gave permission for their data to be used in the preparation of this article. Many thanks also to Isabelle Cauwet and Roland Salut who performed sample imaging for Figure 4 within the FEMTO-ST Institute in Besançon, France.


References


[1] D Scharf , Wideband Multi-detector Color Synthesized Scanning Electron Microscope. U.S. Patent 5,212,383, fi led July 29, 1991, published May 18, 1993.


[2] JI Goldstein et al ., Scanning Electron Microscopy and X-ray Microanalysis , Springer , New York , 2003 .


[3] S Bertazzo et al ., Nature Materials 12 ( 2013 ) 576 – 83 . [4] N Vipin et al ., Microsc Microanal 23 ( 5 ) ( 2017 ) 967 – 77 . [5] R Pintus et al ., Microsc Microanal 14 ( S2 ) ( 2008 ) 608 – 09 . [6] J Paluszynski and W Slowko , T ree dimensional surface reconstruction in Scanning Electron Microscopy , Faculty of Microsystem Electronics and Photonics, Wroclaw University of Technology, http://www.w12.pwr.wroc.pl/ zue/3drece/index.html (accessed March 19, 2018).


2018 May • www.microscopy-today.com


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