Authors:
Stephen Kockentiedt
1
;
Klaus Tönnies
2
;
Erhardt Gierke
3
;
Nico Dziurowitz
3
;
Carmen Thim
3
and
Sabine Plitzko
3
Affiliations:
1
Otto von Guericke University Magdeburg and German Federal Institute for Occupational Safety and Health, Germany
;
2
Otto von Guericke University Magdeburg, Germany
;
3
German Federal Institute for Occupational Safety and Health, Germany
Keyword(s):
Computer Vision, Machine Learning, Nanoparticles, Particle Classification, Scanning Electron Microscopy.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
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 clas
sifier 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.
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