Classification of realisations of random sets

In this paper, the classification task for a family of sets representing the realisation of some random set models is solved. Both unsupervised and supervised classification methods are utilised using the similarity measure between two realisations derived as empirical estimates of $\mathcal N$-distances quantified based on geometric characteristics of the realisations, namely the boundary curvature and the perimeter over area ratios of obtained samples of connected components from the realisations. To justify the proposed methodology, a simulation study is performed using random set models. The methods are used further for classifying histological images of mastopathy and mammary cancer tissue.