Abstract
In this paper, we develop a new semi-automated segmentation method to cancel the unstable blood flow within the left ventricle (LV) in cardiac magnetic resonance (MR) images with parallel imaging. The segmentation is performed using a deformable model driven by a new external energy based on estimated probability density function (pdf) of the MR signal in the LV. The use of signal distribution through the data allows us both to pull the contour towards the myocardium edges and to ensure the smoothness of the curve. Since data for each slice are acquired with the GRAPPA parallel imaging technique, the spatial segmentation is followed by a temporal propagation to improve the convergence in terms of quality and rapidity. Experiments demonstrate that the proposed model provides better results than the results of three state-of-the art methods based on Active Contour Model, which should facilitate the use of the method for clinical purposes. (C) 2017 Elsevier B.V. All rights reserved.