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Bayesian analysis of dynamic magnetic resonance breast images
Author(s) -
Pasquale Francesco,
Barone Piero,
Sebastiani Giovanni,
Stander Julian
Publication year - 2004
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2004.05158.x
Subject(s) - hyperparameter , markov chain monte carlo , computer science , bayesian probability , artificial intelligence , bayesian inference , magnetic resonance imaging , pattern recognition (psychology) , monte carlo method , machine learning , data mining , mathematics , statistics , radiology , medicine
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.