The 3D segmentation of the left ventricle (LV) in cardiac MRI is a challenging problem, due to the presence of other anatomical structures and artifacts (outliers) around the LV. In this paper, a new formulation of a Robust Active Shape Model (RASM) is presented that is able to deal with those outliers. Instead of using the traditional one-to-one mapping of edge points and model points to compute the shape model parameters, the proposed approach uses a one-to-many mapping strategy and groups these edge points into edge segments (strokes). Then, a probabilistic framework provides a robust estimation of the model parameters, in which the influence in the segmentation of the unreliable outliers is reduced. The proposed method was tested on a public dataset comprising 660 volumes. The results indicate that this methodology provides accurate segmentations that are competitive with other state-of-the art methods.