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Stereological estimation of the volume‐weighted mean volume of arbitrary particles observed on random sections*

Gundersen, H. J. G., Jensen, E. B.·1985·Journal of Microscopy

Abstract

SUMMARY: A stereological estimator of the weighted mean volume of particles of arbitrary shape is described. This unbiased estimator is based on simple point‐sampling of linear intercept lengths. The complete absence of shape assumptions effectively breaks the long‐standing ‘convexity‐barrier’: the only requirement here is that individual particles can be unambiguously identified by their profiles on random sections. Practical details of the simple estimation procedure and an example with very irregular particles are reported. Finally, an estimator of the variance of the weighted distribution of particle volume is discussed. This estimator is also valid for particles of arbitrary shape. For any mixture of ellipsoids (spheres, oblates, prolates and triaxial ellipsoids) the estimator is reduced to a simple function of measurements of diameters in the section plane.

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Metadata

Authors
Gundersen, H. J. G., & Jensen, E. B.
Venue
Journal of Microscopy
Publisher
Wiley
Published
1985-05
Volume
138
Issue
2
Pages
127-142
ISSN
  • 0022-2720 (ISSN-L)
  • 1365-2818
OpenAlex
W2078656123
Cited by
489
References
0
FWCI
18.73
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Stereological estimation of the volume‐weighted mean volume of arbitrary particles observed on random sections*. https://app.cochat.ai/papers/10.1111/j.1365-2818.1985.tb02607.x
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Gundersen, H. J. G., & Jensen, E. B. (1985). Stereological estimation of the volume‐weighted mean volume of arbitrary particles observed on random sections*. Journal of Microscopy. https://doi.org/10.1111/j.1365-2818.1985.tb02607.x

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.