  The quantitative characterization of the degree of randomness and aggregation
of surface micro and nanostructures is critical to evaluate their effects on
targeted functionalities. To this end, the methods of Point Pattern Analysis
(PPA), largely used in ecology and medical imaging, seem to provide a powerful
toolset. However, the application of these techniques requires the extraction
of the point pattern of nanostructures from their microscope images. In this
work, we address the issue of the impact that Scanning Electron Microscope
(SEM) image processing may have on the fundamental metric of PPA, i.e. the
Nearest Neighbour Index (NNI). Using as examples two typical SEM images of
polymer micro- and nanostructures taken from secondary and backscattered
electrons, we report the effects of the a) noise filtering and b) binarization
threshold on the value of NNI as well as the impact of the image finite size
effects. Based on these results, we draw conclusions for the safe choice of SEM
settings to provide accurate measurement of nanostructure randomness through
NNI estimation.
