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In this paper an automatic atlas-based segmentation algorithm for 4D cardiac MR images is described. The algorithm is based on the 4D extension of the expectation maximisation (EM) algorithm. The EM algorithm uses a 4D probabilistic cardiac atlas to estimate the initial model parameters and to integrate spatially-varying a-priori information into the classification process. It provides space and time-varying probability maps for the left and right ventricle, the myocardium, and background structures such as the liver, stomach, lungs and skin. The segmentation algorithm also incorporates spatial and temporal contextual information by using 4D Markov Random Fields (MRF). After the classification, the largest connected component of each structure is used as a global connectivity filter that improves the results significantly, especially for the myocardium. Validation against manual segmentations and computation of the correlation between manual and automatic segmentation on 249 3D volumes were calculated. Results show that the procedure can successfully segment the left ventricle (LV) (r=0.96), myocardium (r=0.92) and right ventricle (RV) (r=0.92).