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Application of Clustering-Based Analysis in MRI Brain Tissue Segmentation
The segmentation of brain tissue by MRI not only contributes to the study of the function and anatomical structure of the brain, but it also offers a theoretical foundation for the diagnosis and treatment of brain illnesses. When discussing the anatomy of the brain in a clinical setting, the terms “...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9365576/ https://www.ncbi.nlm.nih.gov/pubmed/35966247 http://dx.doi.org/10.1155/2022/7401184 |
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author | Li, Mingjiang Zhou, Jincheng Wang, Dan Peng, Peng Yu, Yezhao |
author_facet | Li, Mingjiang Zhou, Jincheng Wang, Dan Peng, Peng Yu, Yezhao |
author_sort | Li, Mingjiang |
collection | PubMed |
description | The segmentation of brain tissue by MRI not only contributes to the study of the function and anatomical structure of the brain, but it also offers a theoretical foundation for the diagnosis and treatment of brain illnesses. When discussing the anatomy of the brain in a clinical setting, the terms “white matter,” “gray matter,” and “cerebrospinal fluid” are the ones most frequently used (CSF). However, due to the fact that the human brain is highly complicated in its structure and that there are many different types of brain tissues, the human brain structure of each individual has its own set of distinctive qualities. Because of these several circumstances, the process of segmenting brain tissue will be challenging. In this article, several different clustering algorithms will be discussed, and their performance and effects will be compared to one another. The goal of this comparison is to determine which algorithm is most suited for segmenting MRI brain tissue. Based on the clustering method, the primary emphasis of this research is placed on the segmentation approach that is appropriate for medical brain imaging. The qualitative and quantitative findings of the experiment reveal that the FCM algorithm has more steady performance and better universality, but it is necessary to include the additional auxiliary conditions in order to achieve more ideal outcomes. |
format | Online Article Text |
id | pubmed-9365576 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-93655762022-08-11 Application of Clustering-Based Analysis in MRI Brain Tissue Segmentation Li, Mingjiang Zhou, Jincheng Wang, Dan Peng, Peng Yu, Yezhao Comput Math Methods Med Research Article The segmentation of brain tissue by MRI not only contributes to the study of the function and anatomical structure of the brain, but it also offers a theoretical foundation for the diagnosis and treatment of brain illnesses. When discussing the anatomy of the brain in a clinical setting, the terms “white matter,” “gray matter,” and “cerebrospinal fluid” are the ones most frequently used (CSF). However, due to the fact that the human brain is highly complicated in its structure and that there are many different types of brain tissues, the human brain structure of each individual has its own set of distinctive qualities. Because of these several circumstances, the process of segmenting brain tissue will be challenging. In this article, several different clustering algorithms will be discussed, and their performance and effects will be compared to one another. The goal of this comparison is to determine which algorithm is most suited for segmenting MRI brain tissue. Based on the clustering method, the primary emphasis of this research is placed on the segmentation approach that is appropriate for medical brain imaging. The qualitative and quantitative findings of the experiment reveal that the FCM algorithm has more steady performance and better universality, but it is necessary to include the additional auxiliary conditions in order to achieve more ideal outcomes. Hindawi 2022-08-03 /pmc/articles/PMC9365576/ /pubmed/35966247 http://dx.doi.org/10.1155/2022/7401184 Text en Copyright © 2022 Mingjiang Li 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 Li, Mingjiang Zhou, Jincheng Wang, Dan Peng, Peng Yu, Yezhao Application of Clustering-Based Analysis in MRI Brain Tissue Segmentation |
title | Application of Clustering-Based Analysis in MRI Brain Tissue Segmentation |
title_full | Application of Clustering-Based Analysis in MRI Brain Tissue Segmentation |
title_fullStr | Application of Clustering-Based Analysis in MRI Brain Tissue Segmentation |
title_full_unstemmed | Application of Clustering-Based Analysis in MRI Brain Tissue Segmentation |
title_short | Application of Clustering-Based Analysis in MRI Brain Tissue Segmentation |
title_sort | application of clustering-based analysis in mri brain tissue segmentation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9365576/ https://www.ncbi.nlm.nih.gov/pubmed/35966247 http://dx.doi.org/10.1155/2022/7401184 |
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