Methodological Research on Image Registration Based on Human Brain Tissue In Vivo
As one of the critical steps in brain imaging analysis and processing, brain image registration plays a significant role. In this paper, we proposed a technique of human brain image registration based on tissue morphology in vivo to address the problems of previous image registration. First, differe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/3/738 |
| _version_ | 1797624790464331776 |
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| author | Jiaofen Nan Junya Su Jincan Zhang |
| author_facet | Jiaofen Nan Junya Su Jincan Zhang |
| author_sort | Jiaofen Nan |
| collection | DOAJ |
| description | As one of the critical steps in brain imaging analysis and processing, brain image registration plays a significant role. In this paper, we proposed a technique of human brain image registration based on tissue morphology in vivo to address the problems of previous image registration. First, different feature points were extracted and combined, including those at the boundary of different brain tissues and those of the maximum or minimum from the original image. Second, feature points were screened through eliminating their wrong matching pairs between moving image and reference image. Finally, the remaining matching pairs of feature points were used to generate the model parameters of spatial transformation, with which the brain image registration can be finished by combining interpolation techniques. Results showed that compared with the Surf, Demons, and Sift algorithms, the proposed method can perform better not only for four quantitative indicators (mean square differences, normalized cross correlation, normalized mutual information and mutual information) but also in spatial location, size, appearance contour, and registration details. The findings may suggest that the proposed method will be of great value for brain image reconstruction, fusion, and statistical comparison analysis. |
| first_indexed | 2024-03-11T09:47:26Z |
| format | Article |
| id | doaj.art-f80f0ef59510428fa60232601282a09b |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T09:47:26Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-f80f0ef59510428fa60232601282a09b2023-11-16T16:30:46ZengMDPI AGElectronics2079-92922023-02-0112373810.3390/electronics12030738Methodological Research on Image Registration Based on Human Brain Tissue In VivoJiaofen Nan0Junya Su1Jincan Zhang2School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaSchool of Management Engineering, Zhengzhou University, Zhengzhou 450001, ChinaAs one of the critical steps in brain imaging analysis and processing, brain image registration plays a significant role. In this paper, we proposed a technique of human brain image registration based on tissue morphology in vivo to address the problems of previous image registration. First, different feature points were extracted and combined, including those at the boundary of different brain tissues and those of the maximum or minimum from the original image. Second, feature points were screened through eliminating their wrong matching pairs between moving image and reference image. Finally, the remaining matching pairs of feature points were used to generate the model parameters of spatial transformation, with which the brain image registration can be finished by combining interpolation techniques. Results showed that compared with the Surf, Demons, and Sift algorithms, the proposed method can perform better not only for four quantitative indicators (mean square differences, normalized cross correlation, normalized mutual information and mutual information) but also in spatial location, size, appearance contour, and registration details. The findings may suggest that the proposed method will be of great value for brain image reconstruction, fusion, and statistical comparison analysis.https://www.mdpi.com/2079-9292/12/3/738brain tissue segmentationfeature selectionhuman brainimage registration |
| spellingShingle | Jiaofen Nan Junya Su Jincan Zhang Methodological Research on Image Registration Based on Human Brain Tissue In Vivo Electronics brain tissue segmentation feature selection human brain image registration |
| title | Methodological Research on Image Registration Based on Human Brain Tissue In Vivo |
| title_full | Methodological Research on Image Registration Based on Human Brain Tissue In Vivo |
| title_fullStr | Methodological Research on Image Registration Based on Human Brain Tissue In Vivo |
| title_full_unstemmed | Methodological Research on Image Registration Based on Human Brain Tissue In Vivo |
| title_short | Methodological Research on Image Registration Based on Human Brain Tissue In Vivo |
| title_sort | methodological research on image registration based on human brain tissue in vivo |
| topic | brain tissue segmentation feature selection human brain image registration |
| url | https://www.mdpi.com/2079-9292/12/3/738 |
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