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 -