Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model

L. Cordero-Grande, G. Vegas-Sánchez-Ferrero, P. Casaseca-de-la-Higuera, J. Alberto San-Román-Calvar, Ana Revilla-Orodea, M. Martín-Fernández, C. Alberola-Lopez

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)


A stochastic deformable model is proposed for the segmentation of the myocardium in Magnetic Resonance Imaging. The segmentation is posed as a probabilistic optimization problem in which the optimal time-dependent surface is obtained for the myocardium of the heart in a discrete space of locations built upon simple geometric assumptions. For this purpose, first, the left ventricle is detected by a set of image analysis tools gathered from the literature. Then, the segmentation solution is obtained by the Maximization of the Posterior Marginals for the myocardium location in a Markov Random Field framework which optimally integrates temporal-spatial smoothness with intensity and gradient related features in an unsupervised way by the Maximum Likelihood estimation of the parameters of the field. This scheme provides a flexible and robust segmentation method which has been able to generate results comparable to manually segmented images for some derived cardiac function parameters in a set of 43 patients affected in different degrees by an Acute Myocardial Infarction.

Original languageEnglish
Pages (from-to)283-301
Number of pages19
JournalMedical Image Analysis
Issue number3
Publication statusPublished - Jun 2011
Externally publishedYes


  • Algorithms
  • Artificial Intelligence
  • Computer Simulation
  • Humans
  • Image Enhancement
  • Image Interpretation, Computer-Assisted
  • Imaging, Three-Dimensional
  • Magnetic Resonance Imaging, Cine
  • Markov Chains
  • Models, Biological
  • Models, Cardiovascular
  • Models, Statistical
  • Pattern Recognition, Automated
  • Reproducibility of Results
  • Sensitivity and Specificity


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