Stochastic labelling of biological images

Many hypotheses made by experimental researchers can be formulated as a stochastic labelling of a given image. Some stochastic labelling methods for random closed sets are proposed in this paper. Molchanov (I. Molchanov, 1984, Theor. Probability and Math. Statist. 29 , 113–119) provided the probabilistic background for this problem. However, there is a lack of specific labelling models. Ayala and Simó (G. Ayala and A. Simó, 1995, Advances in Applied Probability 27 , 293–305) proposed a method in which, given the whole set of connected components, every component is classified in a certain phase or category in a completely random way. Alternative methods are necessary in case the random labelling hypothesis is not reliable. A different kind of labelling method is proposed that considers the environment: the type of every connected component is a function of its location.
Two different biphase images are studied: a cross section of a nerve from a rat, and a cross section of an optic nerve from a lizard.