Improved Automatic Recognition of Engineered Nanoparticles in Scanning Electron Microscopy Images

Kockentiedt, Stephen; Tönnies, Klaus D.; Gierke, Erhardt; Dziurowitz, Nico; Thim, Carmen; Plitzko, Sabine

doi:10.5220/0005299003370344

Cited by 1 publication

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“…They found that open, ramified particles absorb sunlight less efficiently than compact, spherical ones, and will, therefore, contribute to global warming to a lesser degree than their spherical counterparts. Other approaches of extracting particle features from images involve additional mathematical transformations, with examples being wavelet (Kockentiedt & Toennies, ) and Fourier descriptors (Rice et al ., ; Fernandez Martinez et al ., ) or the Minkowski functionals and valuations, where particles are interpreted as graphs, with faces, edges, and vertices (Zhang et al ., ). The application of fractal dimensions (Florindo et al ., , ; Florindo & Bruno, ) is another type of approach to analysing particles.…”

Section: Introductionmentioning

confidence: 99%

Scanning electron microscopy image representativeness: morphological data on nanoparticles

Odziomek

Ushizima

Oberbek

et al. 2016

Journal of Microscopy

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A sample of a nanomaterial contains a distribution of nanoparticles of various shapes and/or sizes. A scanning electron microscopy image of such a sample often captures only a fragment of the morphological variety present in the sample. In order to quantitatively analyse the sample using scanning electron microscope digital images, and, in particular, to derive numerical representations of the sample morphology, image content has to be assessed. In this work, we present a framework for extracting morphological information contained in scanning electron microscopy images using computer vision algorithms, and for converting them into numerical particle descriptors. We explore the concept of image representativeness and provide a set of protocols for selecting optimal scanning electron microscopy images as well as determining the smallest representative image set for each of the morphological features. We demonstrate the practical aspects of our methodology by investigating tricalcium phosphate, Ca (PO ) , and calcium hydroxyphosphate, Ca (PO ) (OH), both naturally occurring minerals with a wide range of biomedical applications.

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