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. Sparse representationand dictionary learning models are constructed andpatches 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. 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. 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.