Automated and manual classification of metallic nanoparticles with respect to size and shape by analysis of scanning electron micrographs
Jonas Bals, K. Loza, P. Epple, T. Kircher, Matthias Epple
Abstract
Abstract Automated image analysis has been applied to scanning electron micrographs (transmission mode; STEM) of metallic nanoparticles (silver and gold; about 10 nm to 20 nm). For a reliable particle identification, scanning electron microscopic images must be recorded with distinct contrast and resolution parameters. The particles were separated from the background and classified according to shape and size by machine learning (machine learning). Training images were created with model particles cut out of real electron microscopic images. The automated analysis of the particle size (expressed as area) was well possible, but overlapping particles could not be safely separated. The assignment of particle to six different shape classes (sphere, triangle, square, pentagon, hexagon, rod) by automated analysis was difficult. The fact that real particles never have an ideal geometrical shape but are always distorted or have rough edges or cropped tips is the fundamental reason of this problem. This effect also occurred with human image evaluators and poses a considerable obstacle in the training process for machine learning. Image analysis by machine learning techniques is difficult if different human evaluators disagree on the shape assignment of given particles because a proper training cannot be provided.