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Satisfactory segmentation results of hippocampal subfields are difficult to obtain via most existing multi-atlas segmentation methods due to the tiny volume and complex structure of hippocampus.A segmentation method for hippocampal subfields based on sparse representation and dictionary learning is proposed.Method: Sparse representation and dictionary learning models are constructed and patches are extracted from registered atlases for dictionary learning to determine the label for a voxel in the target image.Besides,local binary patterns(LBP)features of labeled atlases are exploited to improve discrimination of the learned dictionary.The label for the voxel is acquired,after sparse representation of the patch in target image over the learned dictionary is solved.Finally,a correcting method is used for mislabeled voxels,according to priors of atlases.Result: Quantitative and qualitative comparisons demonstrate that the proposed method,which achieves an average Dice Similarity Coefficient(DSC)of 0.890 for the larger hippocampal subfields,outperforms typical approaches based on multi-atlas.Conclusion: The proposed method is suitable to segment hippocampal subfields from MR brain image with higher accuracy and robustness,which provides a favorable basis for the diagnosis of neurodegenerative diseases.