Skip to main content

Bayesian analysis of dynamic magnetic resonance breast images

Buy Article:

$43.00 plus tax (Refund Policy)


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.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

Keywords: Bayesian methods; Classification; Dynamic magnetic resonance imaging; Hyperparameter estimation; Image analysis; Mammography; Restoration; Spatiotemporal models

Document Type: Research Article

Affiliations: 1: 1Consiglio Nazionale delle Ricerche, Rome, Italy, and University of Plymouth, UK 2: 2Consiglio Nazionale delle Ricerche, Rome, Italy 3: 3University of Plymouth, UK

Publication date: 2004-08-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more