In order to make a thorough use of the anatomical and functional information derived from cardiac magnetic resonance images, the epicardium and endocardium of the left ventricle should be extracted in advance. This paper presents a method for segmentation of the endocardium and epicardium of the left ventricle in cardiac magnetic resonance images using Snake models. It first proposes an external force for active contours, which is called convolutional virtual electric field (CONVEF). This CONVEF external force possesses the advantages of enlarged capture range, noise resistance and C-shape concavity convergence and can be implemented in real time by using fast Fourier transform since it is based on convolution. Considering that the left ventricle is roughly a circle, a shape constraint based on circle is adopted for segmentation of the endocardium. As to locating the epicardium, an internal energy based on shape similarity is proposed, and an edge map is coined to calculate the new external force by exploiting the resemblance between the endocardium and epicardium in shape and position. With these strategies, taking the final contour for endocardium as initialization, the Snake contour is reactivated to locate the epicardium automatically and accurately. This paper demonstrates the proposed approach on an in vivo dataset and compare the segmented contours with that of the GGVF (generalized gradient vector flow) Snake and manual collections. The results show its effectiveness.