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 Simo (G. Ayala and A. Simo, 1995, Advances in Applied Probability27, 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.