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Bayesian analysis of dynamic magnetic resonance breast images

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  • Francesco de Pasquale
  • Piero Barone
  • Giovanni Sebastiani
  • Julian Stander

Abstract

Summary. We describe an integrated methodology for analysing dynamic magnetic resonance images of the breast. The problems that motivate this methodology arise from a collaborative study with a tumour institute. The methods are developed within the Bayesian framework and comprise image restoration and classification steps. Two different approaches are proposed for the restoration. Bayesian inference is performed by means of Markov chain Monte Carlo algorithms. We make use of a Metropolis algorithm with a specially chosen proposal distribution that performs better than more commonly used proposals. The classification step is based on a few attribute images yielded by the restoration step that describe the essential features of the contrast agent variation over time. Procedures for hyperparameter estimation are provided, so making our method automatic. The results show the potential of the methodology to extract useful information from acquired dynamic magnetic resonance imaging data about tumour morphology and internal pathophysiological features.

Suggested Citation

  • Francesco de Pasquale & Piero Barone & Giovanni Sebastiani & Julian Stander, 2004. "Bayesian analysis of dynamic magnetic resonance breast images," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(3), pages 475-493, August.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:3:p:475-493
    DOI: 10.1111/j.1467-9876.2004.05158.x
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    Cited by:

    1. Tatiana Tikhonova & Olga Shik, 2008. "Alternative Employment in Rural Area in Russia," Research Paper Series, Gaidar Institute for Economic Policy, issue 114P, pages 224-224.

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