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Measuring the randomness of micro- and nanostructure spatial distributions: Effects of scanning electron microscope
A Mavrogonatos1, E-M Papia1, P Dimitrakellis1
1Institute of Nanoscience and Nanotechnology, NCSR Demokritos, Agia Paraskevi, Greece.
Journal of Microscopy
|October 7, 2022
Summary
Accurate analysis of nanostructure randomness using point pattern analysis (PPA) depends on Scanning Electron Microscope (SEM) image processing. This study reveals how noise filtering and binarisation impact the Nearest Neighbour Index (NNI) for reliable measurements.
Area of Science:
- Materials Science
- Surface Science
- Image Analysis
Background:
- Quantitative characterization of surface micro- and nanostructures is crucial for understanding their functionalities.
- Point Pattern Analysis (PPA) offers a powerful toolset for assessing randomness and aggregation, but requires accurate point pattern extraction from images.
- Scanning Electron Microscope (SEM) imaging is a primary method for visualizing these structures.
Purpose of the Study:
- To investigate the impact of Scanning Electron Microscope (SEM) image processing techniques on the Nearest Neighbour Index (NNI), a key metric in Point Pattern Analysis (PPA).
- To determine how noise filtering and binarisation thresholds affect NNI calculations for surface nanostructures.
- To assess the influence of image finite size effects on NNI measurements.
Main Methods:
- Utilized typical SEM images of polymer micro- and nanostructures acquired using secondary and backscattered electron detectors.
- Systematically analyzed the effects of varying noise filtering levels and binarisation thresholds on NNI values.
- Quantified the impact of image finite size on NNI estimations.
Main Results:
- SEM image processing parameters, specifically noise filtering and binarisation threshold, significantly influence the calculated Nearest Neighbour Index (NNI).
- Image finite size effects also introduce variability into NNI measurements.
- The study quantifies the extent of these impacts on NNI values derived from SEM images.
Conclusions:
- Careful selection of SEM imaging and processing settings is essential for accurate quantitative characterization of nanostructure randomness using PPA.
- Understanding the influence of noise filtering, binarisation, and image size allows for more reliable NNI estimations.
- Provides guidelines for optimizing SEM parameters to ensure accurate measurement of nanostructure spatial distribution.
