Segmentation of Brain MR Images via Sparse Patch Representation

Recently, patch-based segmentation has been proposed for brain MR images. However, the segmentation accuracy of this method depends on similarities over small image patches, which may not be an optimal estimator. In this paper, we propose a new segmentation strategy based on patch reconstruction rather than patch similarity. In the proposed method, the training patch library is considered as a dictionary, and the target patch is modeled as a sparse linear combination of the atoms in the dictionary. The sparse representation is naturally discriminative, which presents an entirely data-driven approach to patchselection and label definition. This Sparse Representation Classification (SRC) strategy produces segmentation results that compare favourably to existing approaches. In addition, a smoothing term is added to the cost function of the sparse coding technique, making the proposed method more robust. To the best of our knowledge, the sparse representation technique has never been used in brain segmentation. In a leave-one-out validation, the proposed method yields a median Dice coefficient of 0.871 for hippocampus on 202 ADNI images, which is competitive compared with state-of-the-art methods.