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

2011; Elsevier BV; Volume: 15; Issue: 3 Linguagem: Inglês

10.1016/j.media.2011.01.002

1361-8431

Lucilio Cordero‐Grande, Gonzalo Vegas‐Sánchez‐Ferrero, Pablo Casaseca‐de‐la‐Higuera, José Alberto San Román, Ana Revilla, Marcos Martı́n-Fernández, Carlos Alberola‐López,

Medical Imaging Techniques and Applications

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.