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The amount of engineered nanoparticles produced each year has grown for some time and will grow in the coming years. However, if such particles are inhaled, they can be toxic. Therefore, to ensure the safety of workers, the nanoparticle concentrations at workplaces have to be measured. This is usually done by gathering the particles in the ambient air and then taking images using scanning electron microscopy. The particles in the images are then manually identified and counted. However, this task takes much time. Therefore, we have developed a system to automatically find and classify particles in these images (Kockentiedt et al., 2012). In this paper, we present an improved version of the system with two new classification feature types. The first are Haralick features. The second is a newly developed feature which estimates the counts of electrons detected by the scanning electron microscopy for each particle. In addition, we have added an algorithm to automatically choose the classifier type and parameters. This way, no expert is needed when the user wants to train the system to recognize a previously unknown particle type. The improved system yields much better results for two types of engineered particles and shows comparable results for a third type.

Improved Automatic Recognition of Engineered Nanoparticles in Scanning Electron Microscopy Images