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

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

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

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.
Original languageEnglish
Title of host publication2014 IEEE International Symposium on Biomedical Imaging
Number of pages4
PublisherIEEE
Publication date2014
Pages157-160
ISBN (Electronic)978-1-4673-1959-1
DOIs
Publication statusPublished - 2014
EventInternational Symposium on Biomedical Imaging - Beijing, China
Duration: 28 Apr 20142 May 2014

Conference

ConferenceInternational Symposium on Biomedical Imaging
LandChina
ByBeijing
Periode28/04/201402/05/2014

ID: 161621598