We propose a fully automated brain tissue segmentation method based on sparse representation of diffusion weighted imaging (DWI) signal. Learning a dictionary from DWI signals, brain voxels are classified into gray matter, white matter, and CSF according to their sparse representation of clustered dictionary atoms. The proposed method was tested on three subjects of the HCP DWI datasets and achieved good agreement with the segmentation on T1-weighted images using SPM12. The method is very fast and robust for a wide range of sparse coding parameter selection and works well on DWI data with less number of shells or gradient directions.
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