Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI
| Autor: | Xiaogang Ren, Yue Wu, Zhiying Cao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Medicine (General)
Article Subject Computer science Biomedical Engineering Hippocampus Neuroimaging Health Informatics R5-920 Medical technology Cluster Analysis Humans Segmentation R855-855.5 Representation (mathematics) Cluster analysis Coefficient matrix Quantitative Biology::Neurons and Cognition business.industry Pattern recognition Magnetic Resonance Imaging Linear subspace Constraint (information theory) Surgery Artificial intelligence business Algorithms Subspace topology Research Article Biotechnology |
| Zdroj: | Journal of Healthcare Engineering, Vol 2021 (2021) Journal of Healthcare Engineering |
| ISSN: | 2040-2309 2040-2295 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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