[1]南姣芬,孟攀婷,童志航,等.基于大脑磁共振成像的多模态多层次信息融合方法[J].郑州大学学报(工学版),2021,42(04):26-32.[doi:10.13705/j.issn.1671-6833.2021.04.006]
　Nan Yanfen,Meng Panting,Tong Zhihang,et al.A Multi-level Information Fusion Method Based on Multimodal Magnetic Resonance Imaging of Human Brain[J].Journal of Zhengzhou University (Engineering Science),2021,42(04):26-32.[doi:10.13705/j.issn.1671-6833.2021.04.006]
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基于大脑磁共振成像的多模态多层次信息融合方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年04期
页码:
26-32
栏目:
出版日期:
2021-07-30

文章信息/Info

Title:
A Multi-level Information Fusion Method Based on Multimodal Magnetic Resonance Imaging of Human Brain
作者:
南姣芬; 孟攀婷; 童志航; 张金灿
郑州轻工业大学计算机与通信工程学院;郑州大学管理工程学院;
Author(s):
Nan Yanfen; Meng Panting; Tong Zhihang; Zhang Jincan;
School of Computer and Communication Engineering, Zhengzhou Light Industry University; School of Management Engineering, Zhengzhou University;
关键词:
Keywords:
multi-level feature; information fusion; multimodal; magnetic resonance imaging
DOI:
10.13705/j.issn.1671-6833.2021.04.006
文献标志码:
A
摘要:
人类大脑的研究对揭示很多疾病的病理学机制存在重要价值，而单模态成像的研究在揭示大脑信息方面具有很大的片面性，因此越来越多的研究人员利用多影像数据间的交叉信息探索大脑奥秘。本文通过多层次特征计算对MCCA + jICA融合技术进行改进，提出一种基于无监督的多层次多模态人脑磁共振图像融合方法。模拟数据结果显示，与现在较为流行MCCA + jICA以及MCCAR + jICA相比，本文方法不仅在检测大脑多模态磁共振图像之间的共变成分上表现良好，还具有更高的稳定性。该方法的提出对更全面地探索大脑奥秘及相关疾病复杂机制具有重要的意义。
Abstract:
An unsupervised multi-level and multimodal fusion method was proposed for human brain magnetic resonance imaging data by multi-level feature calculation based on the improvement of MCCA+ jICA (multimodal canonical correlation analysis+joint independent component analysis).The method preprocessed the raw multimodal data,extracted the low-level features,calculated the high-level features,integrated the multi modal images with multi-level features,and performed fusion analysis with the spatial independent component technique.It was compared with MCCA+ jICA and MCCA+ jICA with reference.Results showed that for different signal-to-noise ratios,the proposed method (95%～99%)had the highest accuracy for detecting the target information,followed by MCCA+ jICA with reference (77%～82%)and MCCA+ jICA (74%～82%),the biggest correlation between the estimation target and the real target (0.890 6),followed by MCCA+ jICA (0.855 7)and MCCA+ jICA with reference (0.699 9),and the lowest standard deviation of the correlation between the mixed matrix from different modalities (0.105 5),followed by MCCA+ jICA with reference (0.138 4)and MCCA+ jICA (0.289 6).Therefore,the method proposed in this paper had a higher accuracy,stronger robustness and better stability in exploring the brain functional-structural co-variation and coupled relationship.This was of great significance to study the brain mechanism and pathophysiology of the brain-related diseases.

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更新日期/Last Update: 2021-08-26