Manual scanning electron microscope (SEM) analysis is historically considered to be slow and tedious resulting in a low volume of data. This is due in large part to the mechanics of moving stage locations and recording image and spectral data. Conversely, high volume data acquired using automated SEM analysis has been associated with the need for complex systems for data management and analysis. In addition, the proliferation of high volume digital microscopy and its attendant “ tonnage” of paper images has lead to the desire for a “green” (filmless and hardcopy-reduced) operation.

There are some classes of projects which are amenable to automated feature analysis - discrete features that are distinct from a background material. However, many projects require operator intervention in order to identify the region or points of interest. Yet, these projects may also require that large data sets be acquired and analyzed for statistical rigor.