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

Author

Sporring, Jon ; Khanmohammadi, Mahdieh ; Darkner, Sune ; Nava, Nicoletta ; Nyengaard, Jens Rondel ; Jensen, Eva Bjorn Wedel

Author_Institution

Dept. of Comput. Sci., Univ. Of Copenhagen, Copenhagen, Denmark

fYear

2014

fDate

April 29 2014-May 2 2014

Firstpage

157

Lastpage

160

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.

Keywords

biological techniques; biological tissues; biology computing; electron microscopy; image reconstruction; statistics; stochastic processes; distance 45 nm; distance function; electron microscopic sections; electron microscopy; image reconstructions; image statistics; isotropic stochastic process; linear algorithm; pixel values; standard deviation; stationary stochastic process; statistical class; statistical image-measures; ultrathin histological section thickness; Estimation; Image reconstruction; Standards; Thickness measurement; Transmission electron microscopy;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on

Conference_Location

Beijing

Type

conf

DOI

10.1109/ISBI.2014.6867833

Filename

6867833