Quite often in the statistical analysis of medical and biological problems, data are images corresponding to entire objects that include smaller objects within them. In these cases, we need models of random closed sets (RACS) confined to compact subsets of the plane. There is no room for stationarity hypotheses and the increase of statistical information comes from independent replicates of the same phenomena rather than increasing our sample window. We investigate practical methods of modelling RACS by means of circumscribed balls, leading to a natural definition of location, size and shape. We discuss the possibilities of using these random variables in order to define statistical spaces of RACS that will allow us to use maximum likelihood methods.
