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There is a high demand for fully automated methods for the analysis ofprimary particle size distributions of agglomerates on transmission electronmicroscopy images. Therefore, a novel method, based on the utilization ofartificial neural networks, was proposed, implemented and validated. Thetraining of the artificial neural networks requires large quantities (up toseveral hundreds of thousands) of transmission electron microscopy images ofagglomerates consisting of primary particles with known sizes. Since the manualevaluation of such large amounts of transmission electron microscopy images isnot feasible, a synthesis of lifelike transmission electron microscopy imagesas training data was implemented. The proposed method can compete withstate-of-the-art automated imaging particle size methods like the Houghtransformation, ultimate erosion and watershed transformation and is in somecases even able to outperform these methods. It is however still outperformedby the manual analysis.

Fully automated primary particle size analysis of agglomerates on transmission electron microscopy images via artificial neural networks