Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm
Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm
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DOI：
10.1016/j.compbiomed.2017.02.011
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发表时间：
2017-04-01
影响因子：
7.7
通讯作者：
Matsuda, Hiroshi
中科院分区：
文献类型：
--
作者：
Beheshti, Iman;Demirel, Hasan;Matsuda, Hiroshi
我们开发了一种新型的计算机辅助诊断(CAD)系统，它使用特征排名和遗传算法来分析结构磁共振成像数据；使用该系统，我们可以在临床诊断前一到三年预测轻度认知障碍(MCI)到阿尔茨海默病(AD)的转化。该CAD系统的开发分四个阶段。首先，我们使用基于体素的形态计量学技术来研究AD组与健康对照组(HCS)的整体和局部灰质(GM)萎缩。GM体积显著减少的区域被分割为感兴趣体积(VOI)。其次，使用这些VOI从AD、HC、稳定型MCI(SMCI)和进展型MCI(PMCI)患者组各自的萎缩区提取体素。然后将体素值提取到特征向量中。第三，在特征选择阶段，所有特征根据各自的t检验分数进行排序，并设计遗传算法来寻找最优特征子集。遗传算法采用Fisher准则作为目标函数的一部分。最后，使用支持向量机进行分类，并进行10次交叉验证。我们通过将其应用于阿尔茨海默病神经成像计划(ADNI)数据集(160名AD、162名HC、65名SMCI和71名pMCI受试者)的基线值来评估所建议的自动CAD系统。实验结果表明，该系统能够区分SMCI和pMCI患者，具有较好的临床实用价值。
We developed a novel computer-aided diagnosis (CAD) system that uses feature-ranking and a genetic algorithm to analyze structural magnetic resonance imaging data; using this system, we can predict conversion of mild cognitive impairment (MCI)-to-Alzheimer's disease (AD) at between one and three years before clinical diagnosis. The CAD system was developed in four stages. First, we used a voxel-based morphometry technique to investigate global and local gray matter (GM) atrophy in an AD group compared with healthy controls (HCs). Regions with significant GM volume reduction were segmented as volumes of interest (VOIs). Second, these VOIs were used to extract voxel values from the respective atrophy regions in AD, HC, stable MCI (sMCI) and progressive MCI (pMCI) patient groups. The voxel values were then extracted into a feature vector. Third, at the feature-selection stage, all features were ranked according to their respective t-test scores and a genetic algorithm designed to find the optimal feature subset. The Fisher criterion was used as part of the objective function in the genetic algorithm. Finally, the classification was carried out using a support vector machine (SVM) with 10-fold cross validation. We evaluated the proposed automatic CAD system by applying it to baseline values from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (160 AD, 162 HC, 65 sMCI and 71 pMCI subjects). The experimental results indicated that the proposed system is capable of distinguishing between sMCI and pMCI patients, and would be appropriate for practical use in a clinical setting.