基于多模态典型相关特征表达的阿尔兹海默病诊断

doi:10.3969/j.issn.0258-8021. 2018. 01.001

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摘要对阿尔兹海默(AD)疾病进程的建模研究,有利于在其早期阶段——轻度认知障碍(MCI)进行更准确的诊断。不仅利用多模态影像数据,还分析模态间特征关系,用于增强与AD/MCI相关的特征表达能力。首先,基于典型相关分析融合不同模态间多个感兴趣区域并生成多模态关系特征表达;其次,基于稀疏最小二乘回归损失函数,以此获得稳定且有识别力的相关性表达特征;最后,使用交叉验证方法将随机选择的训练样本用于支持向量机分类模型,再对测试集受试者进行疾病阶段诊断。实验基于Alzheimer′s Disease Neuroimaging Initiative(ADNI)公共数据库的805位受试者,包括AD,MCI和正常受试者(NC)。此方法对于AD vs NC,MCI vs NC和p-MCI(进行性轻度认知障碍)vs s-MCI(稳定性轻度认知障碍)等3种不同类型数据,诊断结果分别为92.01%,74.83%和70.27%。与其他算法相比,分类准确率都有明显提高。表明所提出的方法能够有效应用于多模态数据对阿尔兹海默病的诊断分析研究。

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	卓奕楠
	杨鹏
	邓云
	倪东
	雷柏英
	汪天富

关键词 ：阿尔兹海默病, 典型相关分析, 多模态, 早期诊断

Abstract：To better explore the underlying disease patterns, a study of modeling the disease procession of Alzheimer′s disease (AD) for early diagnosis of mild cognitive impairment(MCI)is necessary. In this work, we proposed a new method to represent the multi-modal correlations of ROI features for AD diagnosis. First, we applied canonical correlation analysis (CCA) to discover the relationships of ROIs among different modalities and specifically with sparse least square regression loss function to acquire the discriminative features. Then we trained a classification model via support vector machine (SVM) by using the selected features for diagnosis. The empirical studies with 805 subjects downloaded from the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database showed that our method achieved promising improvement when compared with others methods. We achieved 92.01% in AD vs NC (normal control), 74.83% in MCI vs NC and 70.27% in p-MCI (progressive-mild cognitive impairment) vs s-MCI (stable-mild cognitive impairment). In conclusion, the proposed method is beneficial to the early diagnosis of the disease.

Key words： Alzheimer′s disease canonical correlation analysis multi-modal early diagnosis

收稿日期: 2017-03-27

PACS:

R318

基金资助:深圳市基础研究项目(JCYJ20150525092940986)

通讯作者: E-mail: leiby@szu.edu.cn; tfwang@szu.edu.cn

引用本文:

卓奕楠,杨鹏, 邓云,倪东, 雷柏英, 汪天富. 基于多模态典型相关特征表达的阿尔兹海默病诊断[J]. 中国生物医学工程学报, 2018, 37(1): 1-7.
Zhuo Yinan,Yang Peng,Deng Yun, Ni Dong,Lei Baiying,Wang Tianfu. Diagnosis of Alzheimer′s Disease via Multi-Modal Canonical Feature Representation. Chinese Journal of Biomedical Engineering, 2018, 37(1): 1-7.

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http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021. 2018. 01.001 或 http://cjbme.csbme.org/CN/Y2018/V37/I1/1

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