[1]郭圣文,池敏越,岑桂英,等.MR影像体素形态学的阿尔茨海默病自动分类方法[J].东南大学学报(自然科学版),2015,45(2):260-265.[doi:10.3969/j.issn.1001-0505.2015.02.012]
 Guo Shengwen,Chi Minyue,Cen Guiyin,et al.Automatic classification method of Alzheimer’s disease by voxel-based morphometry on MR images[J].Journal of Southeast University (Natural Science Edition),2015,45(2):260-265.[doi:10.3969/j.issn.1001-0505.2015.02.012]
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MR影像体素形态学的阿尔茨海默病自动分类方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
45
期数:
2015年第2期
页码:
260-265
栏目:
生物医学工程
出版日期:
2015-03-20

文章信息/Info

Title:
Automatic classification method of Alzheimer’s disease by voxel-based morphometry on MR images
作者:
郭圣文池敏越岑桂英匡翠立牛传筱赖春任吴效明The Alzheimer’s Disease Neuroimaging Initiative(ADNI)
华南理工大学材料科学与工程学院, 广州510006
Author(s):
Guo Shengwen Chi Minyue Cen Guiyin Kuang Cuili Niu Chuanxiao Lai Chunren Wu Xiaoming The Alzheimer’s Disease Neuroimaging Initiative(ADNI)
School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China
关键词:
阿尔茨海默病 轻度认知功能障碍 体素形态学 支持向量机 递归特征消除
Keywords:
Alzheimer’s disease mild cognitive impairment voxel-based morphometry support vector machine recursive feature elimination
分类号:
R445.2;TP391.41
DOI:
10.3969/j.issn.1001-0505.2015.02.012
摘要:
为了确定轻度认知功能障碍(MCI)与阿尔茨海默病(AD)患者发生萎缩的重要脑区,实现正常老年人(NC)对照组、MCI与AD三组人群的分类,选择了178名被试的脑部MR影像,利用体素形态学与方差分析方法,考察NC,MCI与AD三组人群的MR影像中灰质体积差异;然后,采用递归特征消去法对特征进行降维;最后,利用线性支持向量机对这3种人群进行分类.实验结果表明,MCI组与NC组、MCI组与AD组、AD组与NC组的平均分类准确率分别为(90.2±1.3)%,(74.7±0.9)%,100%.对分类产生重要影响的脑区包括海马、海马旁回、杏仁核、梭状回和嗅皮层等.所提方法不仅能有效揭示NC,MCI,AD三组人群的脑灰质差异,阐明MCI患者与AD患者脑区发生萎缩的过程与特性,而且能准确区分这3组人群,具有显著的临床应用价值.
Abstract:
To determine the atrophy in important brain regions from magnetic resonance(MR)images of patients with Alzheimer’s disease(AD)and mild cognitive impairment(MCI)and classify the normal control(NC),MCI and AD groups, the MR images of 178 subjects were selected for analysis. The voxel-based morphometry(VBM)and analysis of variance(ANOVA)were adopted to investigate the grey matter(GM)volume differences of brain structure in MR images from NC group, MCI group and AD group. Then, the dimension of the detected features was reduced by recursive feature elimination(RFE)method. Finally, the linear support vector machine(LSVM)was applied to classify these three groups. The experimental results show that the average classification accuracies of MCI group and NC group, MCI group and AD group, AD group and NC group are(90.2±1.3)%,(74.7±0.9)% and 100%, respectively. The dominant regions sensitive to classification include hippocampus, parahippocampal gyrus, amygdala, olfactory cortex, fusiform gyrus and so on. The proposed method not only can reveal differences in brain gray matter among NC group, MCI group and AD group effectively, illustrate the shrinking process and characteristics in brain regions of MCI patients and AD patients, but also can exhibit great potentials to accurately distinguish these three groups in clinical application.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2014-10-21.
作者简介: 郭圣文(1971—),男,博士,教授,shwguo@scut.edu.cn.
基金项目: 国家自然科学基金资助项目(31371008, 81171179).
引用本文: 郭圣文,池敏越,岑桂英,等.MR影像体素形态学的阿尔茨海默病自动分类方法[J].东南大学学报:自然科学版,2015,45(2):260-265. [doi:10.3969/j.issn.1001-0505.2015.02.012]
更新日期/Last Update: 2015-03-20