Estimating the thickness of ultra thin sections for electron microscopy by image statistics
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
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 proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
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
T2 - International Symposium on Biomedical Imaging
Y2 - 28 April 2014 through 2 May 2014
ER -
ID: 161621598