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Sparse representation has proven to be a powerful mathematical framework for studying high-dimensional data and uncovering its structures. Some recent research has shown its promise in discriminating image patterns. This paper presents an approach employing sparse appearance representation for segmenting left ventricular endocardial and epicardial boundaries from 2D echocardiographic sequences. It leverages the inherent spatio-temporal coherence of tissue/blood appearance over the sequence by modeling the different appearance of blood and tissues with different appearance dictionaries and updating the dictionaries in a boosting framework as the frames are segmented sequentially. The appearance of each frame is predicted in the form of appearance dictionaries based on the appearance observed in the preceding frames. The dictionaries discriminate image patterns by reconstructing them in the process of sparse coding resulting in an appearance discriminant that we incorporate into a region-based level set segmentation process. We illustrate the advantages of our approach by comparing it to manual tracings and an intensity-prior-based level set method. Experimental results on 34 2D canine echocardiographic sequences show that sparse appearance representation significantly outperforms intensity in terms of reliability and accuracy of segmentation.