Early diagnosis of Alzheimer's disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images
Early diagnosis of Alzheimer's disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images
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DOI：
10.1016/j.neucom.2014.09.072
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发表时间：
2015-03-03
期刊：
影响因子：
6
通讯作者：
Segovia, F.
中科院分区：
文献类型：
--
作者：
Khedher, L.;Ramirez, J.;Segovia, F.
使用功能和结构成像技术的计算机辅助诊断(CAD)系统使医生能够检测出阿尔茨海默病(AD)的早期阶段。为此，磁共振成像(MRI)已被证明是非常有用的评估病理组织在AD。本文提出了一种新的CAD系统，它可以利用组织分割的脑图像来进行AD的早期诊断。该方法旨在区分AD、轻度认知损害(MCI)和老年正常对照(NC)受试者，并基于几种多变量方法，如偏最小二乘(PIS)和主成分分析(PCA)。在这项研究中，来自阿尔茨海默病神经成像计划(ADNI)数据库的188名AD患者、401名MCI患者和229名对照受试者被研究。对每幅图像进行自动脑组织分割，得到灰质(GM)和白质(WM)组织分布。在ADNI数据库上实现了线性或径向基函数(RBF)核的支持向量机分类器来区分正常受试者和AD患者，从而验证了分析方法的有效性。使用k-折交叉技术验证了该方法的性能，其中基于偏最小二乘特征提取和线性支持向量机分类器的系统性能优于主成分分析方法。此外，研究发现，最小二乘特征提取方法能更有效地从数据中提取区分性信息。后者的灵敏度、特异度和准确度分别为85.11%、91.27%和88.49%。(C)2014爱思唯尔B.V.保留所有权利。
Computer aided diagnosis (CAD) systems using functional and structural imaging techniques enable physicians to detect early stages of the Alzheimer's disease (AD). For this purpose, magnetic resonance imaging (MRI) have been proved to be very useful in the assessment of pathological tissues in AD. This paper presents a new CAD system that allows the early AD diagnosis using tissue-segmented brain images. The proposed methodology aims to discriminate between AD, mild cognitive impairment (MCI) and elderly normal control (NC) subjects and is based on several multivariate approaches, such as partial least squares (PIS) and principal component analysis (PCA). In this study, 188 AD patients, 401 MCI patients and 229 control subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were studied. Automated brain tissue segmentation was performed for each image obtaining gray matter (GM) and white matter (WM) tissue distributions. The validity of the analyzed methods was tested on the ADNI database by implementing support vector machine classifiers with linear or radial basis function (RBF) kernels to distinguish between normal subjects and AD patients. The performance of our methodology is validated using k-fold cross technique where the system based on PLS feature extraction and linear SVM classifier outperformed the PCA method. In addition, PLS feature extraction is found to be more effective for extracting discriminative information from the data. In this regard, the developed latter CAD system yielded maximum sensitivity, specificity and accuracy values of 85.11%, 91.27% and 88.49%, respectively. (C) 2014 Elsevier B.V. All rights reserved.