基于MRI图像的阿尔茨海默症患者脑网络特征识别算法

doi:10.11772/j.issn.1001-9081.2019122105

计算机应用 ›› 2020, Vol. 40 ›› Issue (8): 2455-2459.DOI: 10.11772/j.issn.1001-9081.2019122105

• 应用前沿、交叉与综合 • 上一篇下一篇

基于MRI图像的阿尔茨海默症患者脑网络特征识别算法

朱琳¹, 于海涛¹, 雷新宇¹, 刘静², 王若凡³

1. 天津大学电气自动化与信息工程学院, 天津 300072;
2. 唐山市工人医院神经内科, 河北唐山 063000;
3. 天津职业技术师范大学信息技术工程学院, 天津 300222

收稿日期:2019-12-13 修回日期:2020-03-05 出版日期:2020-08-10 发布日期:2020-08-21
通讯作者: 王若凡(1986-),女,河北沧州人,讲师,博士,主要研究方向:神经信息处理,wangrf@tju.edu.cn
作者简介:朱琳(1996-),女,天津人,硕士研究生,主要研究方向:脑认知、神经信息处理;于海涛(1985-),男,河北唐山人,副教授,博士生导师,博士,主要研究方向:脑认知、神经信息处理;雷新宇(1995-),男,河北唐山人,硕士研究生,主要研究方向:脑认知、神经信息处理;刘静(1973-),女,河北唐山人,博士,主要研究方向:神经病学。
基金资助:
国家自然科学基金青年科学基金资助项目（61601331）；天津市自然科学基金资助项目（19JCYBJC18800，18JCQNJC04700）；河北省重点研发项目（18277773D）。

Brain network feature identification algorithm for Alzheimer's patients based on MRI image

ZHU Lin¹, YU Haitao¹, LEI Xinyu¹, LIU Jing², WANG Ruofan³

1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;
2. Department of Neurology, Tangshan Gongren Hospital, Tangshan Hebei 063000, China;
3. School of Information Technology and Engineering, Tianjin University of Technology and Education, Tianjin 300222, China

Received:2019-12-13 Revised:2020-03-05 Online:2020-08-10 Published:2020-08-21
Supported by:
This work is partially supported by the Youth Program of National Natural Science Foundation of China (61601331), the Tianjin Natural Science Foundation (19JCYBJC18800, 18JCQNJC04700), the Hebei Key Research and Development Project (18277773D).

摘要/Abstract

摘要： 针对通过脑成像对阿尔茨海默症（AD）进行人工识别存在主观性、易误诊的问题，提出了一种基于核磁共振成像（MRI）图像构建脑网络对AD进行自动识别的方法。首先，把MRI图像叠加并进行结构块划分，并通过计算任意两个结构块之间的结构相似性（SSIM）来构造网络；然后，利用复杂网络理论提取结构参数，并将其作为机器学习算法的输入实现AD的自动识别。分析发现双参数特别是节点介数和边介数作为输入时分类效果最优，进一步研究发现MRI图像划分为27个结构块时分类效果最优，对于加权网络和无权网络的准确率分别最高可达91.04%和94.51%。实验结果表明，基于MRI结构块划分构建的结构相似性复杂网络能够对AD进行准确率更高的识别。

关键词: 脑网络, 磁共振成像, 机器学习, 阿尔茨海默症, 结构相似性

Abstract: In view of the problem of subjectivity and easy misdiagnosis in the artificial identification of Alzheimer's Disease (AD) through brain imaging, a method of automatic identification of AD by constructing brain network based on Magnetic Resonance Imaging (MRI) image was proposed. Firstly, MRI images were superimposed and were divided into structural blocks, and the Structural SIMilarity (SSIM) between any two structural blocks was calculated to construct the network. Then, the complex network theory was used to extract structural parameters, which were used as the input of machine learning algorithm to realize the AD automatic identification. The analysis found that the classification effect was optimal with two parameters, especially the node betweenness and edge betweenness were taken as the input. Further study found that the classification effect was optimal when MRI image was divided into 27 structural blocks, and the accuracy of weighted network and unweighted network was up to 91.04% and 94.51% respectively. The experimental results show that the complex network of structural similarity based on MRI block division can identify AD with higher accuracy.

Key words: brain network, Magnetic Resonance Imaging (MRI), machine learning, Alzheimer's Disease (AD), structural similarity

中图分类号:

TP391.5

朱琳, 于海涛, 雷新宇, 刘静, 王若凡. 基于MRI图像的阿尔茨海默症患者脑网络特征识别算法[J]. 计算机应用, 2020, 40(8): 2455-2459.

ZHU Lin, YU Haitao, LEI Xinyu, LIU Jing, WANG Ruofan. Brain network feature identification algorithm for Alzheimer's patients based on MRI image[J]. Journal of Computer Applications, 2020, 40(8): 2455-2459.

参考文献

[1] MATTSON M P. Pathways towards and away from Alzheimer's disease[J]. Nature, 2016, 430(7000):631-639.
[2] KLUNK W E, HENRY ENGLER H, NORDBERG A, et al. Imaging brain amyloid in Alzheimer's disease with Pittsburgh compound-B[J]. Annals of Neurology, 2004, 55(3):306-319.
[3] BLENNOW K. Biomarkers in Alzheimer's disease drug development[J]. Alzheimer's and Dementia, 2011, 7(3):e13-e44.
[4] 王若凡,刘静,王江,等. 基于功率谱及有限穿越可视图的癫痫脑电信号分析算法[J]. 计算机应用, 2017, 37(1):175-182. (WANG R F, LIU J, WANG J, et al. Analysis algorithm of electroencephalogram signals for epilepsy diagnosis based on power spectral density and limited penetrable visibility graph[J]. Journal of Computer Applications, 2017, 37(1):175-182.)
[5] BATEMAN R J, XIONG C, BENZINGER T L S, et al. Clinical and biomarker changes in dominantly inherited Alzheimer's disease[J]. New England Journal of Medicine, 2012, 367(9):795-804.
[6] HERRMANN C S, DEMIRALP T. Human EEG gamma oscillations in neuropsychiatric disorders[J]. Clinical Neurophysiology, 2005, 116(12):2719-2733.
[7] BABILONI C, FERRI R, BINETTI G, et al. Directionality of EEG synchronization in Alzheimer's disease subjects[J]. Neurobiology of Aging, 2009, 30(1):93-102.
[8] DAUWELS J, VIALATTE F, CICHOCKI A. Diagnosis of Alzheimers disease from EEG signals:where are we standing?[J]. Current Alzheimer Research, 2010, 7(6):487-505.
[9] SAKKALIS V. Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG[J]. Computers in Biology and Medicine, 2011, 41(12):1110-1117.
[10] 林伟铭,高钦泉,杜民. 卷积神经网络诊断阿尔兹海默症的方法[J].计算机应用, 2017, 37(12):3504-3508. (LIN W M, GAO Q Q, DU M, Convolutional neural network based method for diagnosis of Alzheimer's disease[J]. Journal of Computer Applications, 2017, 37(12):3504-3508.)
[11] WALLIN A, BLENNOW K. Clinical diagnosis of Alzheimer's disease by primary care physicians and specialists[J]. Acta Neurologica Scandinavica, 1992, 85(S139):26-31.
[12] TRAMBAIOLLI L R, LORENA A C, FRAGA F J, et al. Improving Alzheimer's disease diagnosis with machine learning techniques[J]. Clinical EEG and Neuroscience, 2011, 42(3):160-165.
[13] ÇEVIK A, WEBER G W, EYÜBOĞLU B M, et al. Voxel-MARS:a method for early detection of Alzheimer's disease by classification of structural brain MRI[J]. Annals of Operations Research, 2017, 258(1):31-57.
[14] DE WAAL H, STAM C J, BLANKENSTEIN M A, et al. EEG abnormalities in early and late onset Alzheimer's disease:understanding heterogeneity[J]. Journal of Neurology, Neurosurgery, and Psychiatry, 2011, 82(1):67-71.
[15] LIU Y, WANG K, YU C, et al. Regional homogeneity, functional connectivity and imaging markers of Alzheimer's disease:a review of resting-state fMRI studies[J]. Neuropsychologia, 2008, 46(6):1648-1656.
[16] CHA J, HWANG J M, JO H J, et al. Assessment of functional characteristics of amnestic mild cognitive impairment and Alzheimer's disease using various methods of resting-state fMRI analysis[J]. BioMed Research International, 2015, 2015:No.907464.
[17] SANZ-ARIGITA E J, SCHOONHEIM M M, DAMOISEAUX J S, et al. Loss of ‘small-world’ networks in Alzheimer's disease:graph analysis of fMRI resting-state functional connectivity[J]. PLoS ONE, 2010, 5(11):No.e13788.
[18] 郝晓弘,金亚蓉,马宇,等. 基于遗传算法的模糊迭代学习控制算法[J]. 计算机应用, 2013, 33(4):960-963. (HAO X H, JIN Y R, MA Y, et al. Fuzzy iterative learning control based on genetic algorithm[J]. Journal of Computer Applications, 2013, 33(4):960-963.)
[19] ANVAR S, STEVENS J R, CORCORAN C, et al. Accounting for dependence induced by weighted KNN imputation in paired samples, motivated by a colorectal cancer study[J]. PLoS ONE, 2015, 10(4):No.e0119876.
[20] MITCHELL T M. Machine Learning[M]. New York:McGraw-Hill, 1997:165-175.
[21] 文志诚,曹春丽,周浩.基于朴素贝叶斯分类器的网络安全态势评估方法[J].计算机应用,2015,35(8):2164-2168. (WEN Z C, CAO C L, ZHOU H. Network security situation assessment method based on Naive Bayes classifier[J]. Journal of Computer Applications, 2015, 35(8):2164-2168.)

基于MRI图像的阿尔茨海默症患者脑网络特征识别算法

Brain network feature identification algorithm for Alzheimer's patients based on MRI image

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