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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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Detalles Bibliográficos
Autores principales: Ren, Xiaogang, Wu, Yue, Cao, Zhiying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8487389/
https://www.ncbi.nlm.nih.gov/pubmed/34608408
http://dx.doi.org/10.1155/2021/3937222
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author Ren, Xiaogang
Wu, Yue
Cao, Zhiying
author_facet Ren, Xiaogang
Wu, Yue
Cao, Zhiying
author_sort Ren, Xiaogang
collection PubMed
description 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.
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spelling pubmed-84873892021-10-03 Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI Ren, Xiaogang Wu, Yue Cao, Zhiying J Healthc Eng Research Article 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. Hindawi 2021-09-25 /pmc/articles/PMC8487389/ /pubmed/34608408 http://dx.doi.org/10.1155/2021/3937222 Text en Copyright © 2021 Xiaogang Ren et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ren, Xiaogang
Wu, Yue
Cao, Zhiying
Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI
title Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI
title_full Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI
title_fullStr Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI
title_full_unstemmed Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI
title_short Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI
title_sort hippocampus segmentation method based on subspace patch-sparsity clustering in noisy brain mri
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8487389/
https://www.ncbi.nlm.nih.gov/pubmed/34608408
http://dx.doi.org/10.1155/2021/3937222
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AT caozhiying hippocampussegmentationmethodbasedonsubspacepatchsparsityclusteringinnoisybrainmri