Gibbs distribution-based Bayesian segmentation of electron microscopy nanostructure images
Б. Н. Грудин, Vladimir S. Plotnikov, Е. В. Пустовалов, S. V. Polischuk, N. A. Smolyaninov, A. A. Efremov · Bulletin of the Russian Academy of Sciences Physics · 2013
The use of Gibbs distribution-based Bayesian segmentation of electron microscopy images for visualizing nanostructures is investigated. Bayesian segmentation involves dividing an image into nonoverlapping areas that correspond as closely as possible to the observed image. A quantitative characteristic of this correspondence is the a posteriori probability of one variant of division or another. The most likely version is always the division with the greatest a posteriori probability. The Metropolis algorithm for stochastic relaxation is used to obtain Bayesian estimates of the a posteriori probability of a division. Our study of Bayesian segmentation requires visualization of nanostructures on an electron microscopy image of a film made of NiW nanocrystalline alloy.