KPCA和Adaboost算法在阿尔茨海默症功能磁共振影像分类中的应用

Application of KPCA and Adaboost algorithm in the classification of functional magnetic resonance images of Alzheimer’s disease

本研究的目的在于使用机器学习方法,对脑部功能磁共振成像数据进行分析与特征提取,完成对阿尔茨海默症(AD)的辅助诊断与分析。首先对数据进行预处理与去除协变量,并从大脑全局特征出发,根据现有的自动解剖标记模板,把每个被试的大脑分为116个脑区,通过提取每个脑区的时间序列,构建全脑功能连接矩阵,然后使用核主成分分析法进行特征提取,最后用Adaboost算法进行分类。在对34名AD患者、35名轻度认知障碍患者和35名正常对照组的功能磁共振成像数据进行的实验结果表明,利用静息态功能磁共振成像,同时结合机器学习的方法,能够有效地实现AD的正确分类,准确率可以达到96%,该结果可以为AD患者的临床辅助诊断提供有效的判断依据。

The purpose of this study is to achieve the auxiliary diagnosis and analysis of Alzheimer's disease (AD) by analyzing and characterizing brain functional magnetic resonance imaging (fMRI) data using machine learning method.After the fMRI data is preprocessed and the covariate is removed,the brain of each subject is divided into 116 brain regions according to anatomical automatic labeling template,and the whole brain functional connection matrix is constructed by extracting the time series of each brain region.Kernel principal component analysis is used to extract features and Adaboost algorithm is used for classification.The results of the experiment on fMRI images of 34 patients with AD,35 patients with mild cognitive impairments and 35 normal controls show that using resting state fMRI combined with machine learning method can effectively realize the accurate classification of AD,with a classification accuracy rate up to 96%.The proposed method can provide an effective basis for the auxiliary diagnosis of patients with AD.