Estimating the thickness of ultra thin sections for electron microscopy by image statistics

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Estimating the thickness of ultra thin sections for electron microscopy by image statistics. / Sporring, Jon; Khanmohammadi, Mahdieh; Darkner, Sune; Nava, Nicoletta; Nyengaard, Jens Randel; Jensen, Eva B. Vedel.

2014 IEEE International Symposium on Biomedical Imaging. IEEE, 2014. p. 157-160.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Harvard

Sporring, J, Khanmohammadi, M, Darkner, S, Nava, N, Nyengaard, JR & Jensen, EBV 2014, Estimating the thickness of ultra thin sections for electron microscopy by image statistics. in 2014 IEEE International Symposium on Biomedical Imaging. IEEE, pp. 157-160, International Symposium on Biomedical Imaging, Beijing, China, 28/04/2014. https://doi.org/10.1109/ISBI.2014.6867833

APA

Sporring, J., Khanmohammadi, M., Darkner, S., Nava, N., Nyengaard, J. R., & Jensen, E. B. V. (2014). Estimating the thickness of ultra thin sections for electron microscopy by image statistics. In 2014 IEEE International Symposium on Biomedical Imaging (pp. 157-160). IEEE. https://doi.org/10.1109/ISBI.2014.6867833

Vancouver

Sporring J, Khanmohammadi M, Darkner S, Nava N, Nyengaard JR, Jensen EBV. Estimating the thickness of ultra thin sections for electron microscopy by image statistics. In 2014 IEEE International Symposium on Biomedical Imaging. IEEE. 2014. p. 157-160 https://doi.org/10.1109/ISBI.2014.6867833

Author

Sporring, Jon ; Khanmohammadi, Mahdieh ; Darkner, Sune ; Nava, Nicoletta ; Nyengaard, Jens Randel ; Jensen, Eva B. Vedel. / Estimating the thickness of ultra thin sections for electron microscopy by image statistics. 2014 IEEE International Symposium on Biomedical Imaging. IEEE, 2014. pp. 157-160

Bibtex

@inproceedings{82fb2cb15fbc4bef86aff2ceebc93f4c,
title = "Estimating the thickness of ultra thin sections for electron microscopy by image statistics",
abstract = "We propose a method for estimating the thickness of ultra thin histological sections by image statistics alone. Our method works for images, that are the realisations of a stationary and isotropic stochastic process, and it relies on the existence of statistical image-measures that are strictly monotonic with distance. We propose to use the standard deviation of the difference between pixel values as a function of distance, and we give an extremely simple, linear algorithm. Our algorithm is applied to the challenging domain of electron microscopic sections supposedly $45\text{ nm}$ apart, and we show that these images with high certainty belong to the required statistical class, and that the reconstructions are valid.",
author = "Jon Sporring and Mahdieh Khanmohammadi and Sune Darkner and Nicoletta Nava and Nyengaard, {Jens Randel} and Jensen, {Eva B. Vedel}",
year = "2014",
doi = "10.1109/ISBI.2014.6867833",
language = "English",
pages = "157--160",
booktitle = "2014 IEEE International Symposium on Biomedical Imaging",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Estimating the thickness of ultra thin sections for electron microscopy by image statistics

AU - Sporring, Jon

AU - Khanmohammadi, Mahdieh

AU - Darkner, Sune

AU - Nava, Nicoletta

AU - Nyengaard, Jens Randel

AU - Jensen, Eva B. Vedel

PY - 2014

Y1 - 2014

N2 - We propose a method for estimating the thickness of ultra thin histological sections by image statistics alone. Our method works for images, that are the realisations of a stationary and isotropic stochastic process, and it relies on the existence of statistical image-measures that are strictly monotonic with distance. We propose to use the standard deviation of the difference between pixel values as a function of distance, and we give an extremely simple, linear algorithm. Our algorithm is applied to the challenging domain of electron microscopic sections supposedly $45\text{ nm}$ apart, and we show that these images with high certainty belong to the required statistical class, and that the reconstructions are valid.

AB - We propose a method for estimating the thickness of ultra thin histological sections by image statistics alone. Our method works for images, that are the realisations of a stationary and isotropic stochastic process, and it relies on the existence of statistical image-measures that are strictly monotonic with distance. We propose to use the standard deviation of the difference between pixel values as a function of distance, and we give an extremely simple, linear algorithm. Our algorithm is applied to the challenging domain of electron microscopic sections supposedly $45\text{ nm}$ apart, and we show that these images with high certainty belong to the required statistical class, and that the reconstructions are valid.

U2 - 10.1109/ISBI.2014.6867833

DO - 10.1109/ISBI.2014.6867833

M3 - Article in proceedings

SP - 157

EP - 160

BT - 2014 IEEE International Symposium on Biomedical Imaging

PB - IEEE

ER -

ID: 161621598