Bayesian segmentation of nanostructures images

In this paper we investigate the application of Bayesian segmentation of electron microscopic images based on the Gibbs distribution for visualization of nanostructures. The nature of the Bayesian segmentation is a partition of the image into non overlapping regions, which is the most consistent with the observed image. Quantitative characteristic of this correspondence is a posteriori probability of a particular variant of the partition. The most likely partition is a partition with maximized posteriori probability.