  There is a high demand for fully automated methods for the analysis of
primary particle size distributions of agglomerates on transmission electron
microscopy images. Therefore, a novel method, based on the utilization of
artificial neural networks, was proposed, implemented and validated. The
training of the artificial neural networks requires large quantities (up to
several hundreds of thousands) of transmission electron microscopy images of
agglomerates consisting of primary particles with known sizes. Since the manual
evaluation of such large amounts of transmission electron microscopy images is
not feasible, a synthesis of lifelike transmission electron microscopy images
as training data was implemented. The proposed method can compete with
state-of-the-art automated imaging particle size methods like the Hough
transformation, ultimate erosion and watershed transformation and is in some
cases even able to outperform these methods. It is however still outperformed
by the manual analysis.
