Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI

Since the hippocampus is of small size, low contrast, and irregular shape, a novel hippocampus segmentation method based on subspace patch-sparsity clustering in brain MRI is proposed to improve the segmentation accuracy, which requires that the representation coefficients in different subspaces sho...

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Bibliographic Details
Published in:Journal of healthcare engineering 2021-09, Vol.2021, p.3937222-10
Main Authors: Ren, Xiaogang, Wu, Yue, Cao, Zhiying
Format: Article
Language:English
Subjects:
Algorithms
Cluster Analysis
Hippocampus - diagnostic imaging
Humans
Magnetic Resonance Imaging
Neuroimaging
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Summary:Since the hippocampus is of small size, low contrast, and irregular shape, a novel hippocampus segmentation method based on subspace patch-sparsity clustering in brain MRI is proposed to improve the segmentation accuracy, which requires that the representation coefficients in different subspaces should be as sparse as possible, while the representation coefficients in the same subspace should be as average as possible. By restraining the coefficient matrix with the patch-sparse constraint, the coefficient matrix contains a patch-sparse structure, which is helpful to the hippocampus segmentation. The experimental results show that our proposed method is effective in the noisy brain MRI data, which can well deal with hippocampus segmentation problem.
ISSN:2040-2295
2040-2309
DOI:10.1155/2021/3937222