1. Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model
- Author
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Cordero-Grande, L., Vegas-Sánchez-Ferrero, G., Casaseca-de-la-Higuera, P., Alberto San-Román-Calvar, J., Revilla-Orodea, Ana, Martín-Fernández, M., and Alberola-López, C.
- Subjects
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IMAGE analysis , *MARKOV random fields , *MATHEMATICAL models , *MAGNETIC resonance imaging , *HEART function tests ,MYOCARDIAL infarction diagnosis - Abstract
Abstract: 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. [Copyright &y& Elsevier]
- Published
- 2011
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