Journal
MEDICAL IMAGE ANALYSIS
Volume 15, Issue 3, Pages 283-301Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2011.01.002
Keywords
Magnetic resonance imaging; Markov Random Field; Unsupervised segmentation; Cardiac function; Acute myocardial infarction
Categories
Funding
- Fondo de Investigaciones Sanitarias [PI04-1483]
- Ministerio de Ciencia e Innovacion
- Fondo Europeo de Desarrollo Regional (FEDER) [TEC2007-67073/TCM]
- Centro para el Desarrollo Tecnologico Industrial (CDTI) [CEN-20091044]
- Junta de Castilla y Leon [VA027A07, VA039A10-2]
- Consejeria de Sanidad de Castilla y Leon [GRS 292/A/08, SAN126/VA032/09, SAN126/VA033/09]
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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. (C) 2011 Elsevier B.V. All rights reserved.
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