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 Citations (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.

Original language	English
Title of host publication	2014 IEEE International Symposium on Biomedical Imaging
Number of pages	4
Publisher	IEEE
Publication date	29 Jul 2014
Pages	157-160
ISBN (Electronic)	978-1-4673-1959-1
DOIs	https://doi.org/10.1109/isbi.2014.6867833
Publication status	Published - 29 Jul 2014
Event	International Symposium on Biomedical Imaging - Beijing, China Duration: 28 Apr 2014 → 2 May 2014

Conference

Conference	International Symposium on Biomedical Imaging
Country/Territory	China
City	Beijing
Period	28/04/2014 → 02/05/2014

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

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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

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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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AU - Sporring, Jon

AU - Khanmohammadi, Mahdieh

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AU - Nava, Nicoletta

AU - Nyengaard, Jens Randel

AU - Jensen, Eva B. Vedel

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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.

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M3 - Article in proceedings

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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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