Scanning Electron Microscopy

Figure 4 : Images of a roughness reference specimen. (a) Two SEM images taken at different angles to the specimen surface. (b) Metrologically accurate topographic reconstruction obtained from the stereo pair, presented in perspective format and colored with a false color shading. (c) Height profi le corresponding to a cross section of the 3D topography built by the stereophotogrammetry.

density-dependent color scanning electron micrograph (DDC- SEM) [ 3 ].

Manual colorization . Another approach for SE images is to manually colorize objects. Once an SEM image is obtained, researchers may spend considerable time identifying, isolating, and colorizing each type of object so that readers of their publications are able to instinctively comprehend and interpret them. T e problem, of course, is that this is usually a tedious and time-consuming operation. Each object has to be manually separated from the others, for example using image-editing soſt ware. Semi-automated colorization . One recent technique allows users to colorize images quickly and easily. MountainsMap SEM  allows automatic or semi-automatic object identifi cation and segmentation. Selecting an object can oſt en be achieved with just one click of the mouse. T e technology behind this step involves over 30 successive mathematical operations to distinguish the various objects in the image ( Figure 3 ). T e fi nal image in Figure 3 could potentially take hours to produce using photo-editing soſt ware. Of course, whether or not two adjacent objects should be separated can be open to interpre- tation. For instance, should a blackberry be considered one fruit or many fruits? (Not sure our ancestors asked themselves this before eating them . . .). T ankfully, the soſt ware allows users to defi ne and modify boundaries around objects. T is is accomplished by varying the settings on object size fi lters, by using free border editing (mouse drawing and cutting), or by picking from among secondary boundaries (as one would diff erentiate state borders from county boundaries within the state). Of course, the example presented here ( Figure 3 ) is a relatively easy one. Not all objects can be picked up in a single


mouse click. But even if three or four clicks are necessary to indicate to the soſt ware that certain parts go together, it is still much quicker than having to draw around objects manually. Furthermore, in the case of multiple similar objects, the

soſt ware can fi nd them automatically and allocate a color to each depending upon its size or shape. For example, on a black and white photograph, it would color all the rice white and the peas green automatically based on their shape. In another image, it would color all the grapefruits in yellow and all the tangerines in orange based on their size, once an example of each has been shown as a model.

Leaving the Flat World From 2D to 3D . T ere are several methods for converting standard two-dimensional (SE) images into 3D-like images (that is, those representing X , Y , and Z (height) coordinates). Color can be closely linked to this process. Stereophotogrammetry . T e most metrologically accurate approach to 3D reconstruction of surface features is stereophoto- grammetry. T is technique consists of acquiring two SEM images of the same object from diff erent angles. Height information can then be calculated trigonometrically. T e only drawbacks of this method are that it requires having an SEM that allows specimen tilt and calls for two successive images of the sample to be taken ( Figure 4a ). A number of commercial and non-commercial soſt ware solutions are available to interpret the 3D information in the stereo pair. Figure 4b shows a perspective reconstruction produced by MountainsMap SEM  . T e topography obtained allows the quantifi cation of surface features (step height, volume, angles, fl atness). T is 3D representation was obtained by (1) calculating a topography map from the stereo reconstruction, (2) creating a • 2018 May

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