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

Jon Sporring; Mahdieh Khanmohammadi; Sune Darkner; Nicoletta Nava; Jens Randel Nyengaard; Eva B. Vedel Jensen

doi:10.1109/isbi.2014.6867833

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

Jon Sporring, Mahdieh Khanmohammadi, Sune Darkner, Nicoletta Nava, Jens Randel Nyengaard, Eva B. Vedel Jensen

4 Citationer (Scopus)

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 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 give an extremely simple, linear algorithm. Our algorithm is applied to the challenging domain of electron microscopic sections supposedly 45 nm apart, and we show that these images with high certainty belong to the required statistical class, and that the reconstructions are valid.

Originalsprog	Engelsk
Titel	2014 IEEE International Symposium on Biomedical Imaging
Antal sider	4
Forlag	IEEE
Publikationsdato	29 jul. 2014
Sider	157-160
ISBN (Elektronisk)	978-1-4673-1959-1
DOI	https://doi.org/10.1109/isbi.2014.6867833
Status	Udgivet - 29 jul. 2014
Begivenhed	International Symposium on Biomedical Imaging - Beijing, Kina Varighed: 28 apr. 2014 → 2 maj 2014

Konference

Konference	International Symposium on Biomedical Imaging
Land/Område	Kina
By	Beijing
Periode	28/04/2014 → 02/05/2014

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10.1109/isbi.2014.6867833

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6867833

Citationsformater

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. i 2014 IEEE International Symposium on Biomedical Imaging. IEEE, s. 157-160, International Symposium on Biomedical Imaging, Beijing, Kina, 28/04/2014. https://doi.org/10.1109/isbi.2014.6867833

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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 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 give an extremely simple, linear algorithm. Our algorithm is applied to the challenging domain of electron microscopic sections supposedly 45 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}",

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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 - 2014/7/29

Y1 - 2014/7/29

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 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 give an extremely simple, linear algorithm. Our algorithm is applied to the challenging domain of electron microscopic sections supposedly 45 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 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 give an extremely simple, linear algorithm. Our algorithm is applied to the challenging domain of electron microscopic sections supposedly 45 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

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BT - 2014 IEEE International Symposium on Biomedical Imaging

PB - IEEE

T2 - International Symposium on Biomedical Imaging

Y2 - 28 April 2014 through 2 May 2014

ER -

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

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