Reversible Jump MCMC Methods and Segmentation Algorithms in Hidden Markov Models

Roberta Paroli, Luigi Spezia

Research output: Contribution to journalArticle


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 a further aim and is performed by means of a number of competing algorithms. We apply our Bayesian inference and segmentation tools to digital images, which are linearized through the Peano–Hilbert scan, and perform experiments and comparisons on both synthetic images and a real brain magnetic resonance image.
Original languageEnglish
Pages (from-to)151-166
Number of pages16
Publication statusPublished - 2010


  • MCMC
  • Markov random filed
  • Segmentation


Dive into the research topics of 'Reversible Jump MCMC Methods and Segmentation Algorithms in Hidden Markov Models'. Together they form a unique fingerprint.

Cite this