• L Cordero-Grande, G Vegas-Sánchez-Ferrero, P Casaseca-de-la-Higuera, J Alberto San-Román-Calvar, Ana Revilla-Orodea, M Martín-Fernández, and C Alberola-López.
• Laboratorio de Procesado de Imagen, Escuela Técnica Superior de Ingenieros de Telecomunicación, Campus Miguel Delibes s.n., 47011 Valladolid, Spain. lcorgra@lpi.tel.uva.es
• Med Image Anal. 2011 Jun 1; 15 (3): 283-301.

AbstractA 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 © 2011 Elsevier B.V. All rights reserved.

26 keywords...

hide…