In this review paper, we summarized the automated dementia identification algorithms in the literature from a pattern classification perspective. Since most of those algorithms consist of both feature extraction and classification, we provide a survey on three categories of feature extraction methods, including the voxel-, vertex- and ROI-based ones, and four categories of classifiers, including the linear discriminant analysis, Bayes classifiers, support vector machines, and artificial neural networks. We also compare the reported performance of many recently published dementia identification algorithms. Our comparison shows that many algorithms can differentiate the Alzheimer's disease (AD) from elderly normal with a largely satisfying accuracy, whereas distinguishing the mild cognitive impairment from AD or elderly normal still remains a major challenge.

中文翻译：

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使用医学影像自动识别痴呆症：从模式分类角度进行的调查。

在这篇综述文章中，我们从模式分类的角度总结了文献中的自动化痴呆症识别算法。由于这些算法大多数都包含特征提取和分类，因此我们对特征提取方法的三类进行了调查，其中包括基于体素，基于顶点和感兴趣区的方法，以及四类分类器，包括线性判别分析，贝叶斯分类器，支持向量机和人工神经网络。我们还比较了许多最近发表的痴呆症识别算法的报告性能。我们的比较结果表明，许多算法可以以令人满意的准确性将老年痴呆症（AD）与老年正常人区分开来，