We consider hidden Markov Models with an unknown number of regimes for the segmentation of the pixel intensities of digital images that consist of a small set of colours. New reversible jump Markov chain Monte Carlo algorithms to estimate both the dimension and the unknown parameters of the model are introduced. Parameters are updated by random walk Metropolis-Hastings moves, without updating the sequence of the hidden Markov chain. The segmentation, i.e. the estimation of the hidden regimes, is another aim and it is performed by means of some competing algorithms. We apply our Bayesian inference and segmentation tools to digital images, which are linearized through the Peano-Hilbert scan, and make experiments and comparisons on both synthetic images and a real brain magnetic resonance image.