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Accurate delineation of the left ventricular myocardial boundaries on cardiac cine magnetic resonance (MR) images is essential for volumetric and functional cardiac analysis. Automated myocardial contour delineation often suffers from misalignment of slices, nonuniform coil sensitivity, blood-flow-related inter- and intraslice intensity inhomogeneities, blurring due to motion, partial voluming, and a need to circumscribe the papillary muscles and the trabeculae. In this paper, we propose a novel method for data-driven localization and segmentation of the left ventricle in the cine-MR images toward automated computation of ejection fraction (EF). Our hybrid segmentation method combines intensity- and texture-based fuzzy affinity maps obtained from a novel multiclass, multifeature fuzzy connectedness method with dynamic-programming-based boundary detection to delineate the myocardial contours. Bland-Altman analysis indicates that the mean biases of the end-diastolic volume, end-systolic volume, and EF estimates of our method are comparable to the interobserver variability when compared with the annotations from two experts.