Automatic tool for Alzheimer's disease diagnosis using PCA and Bayesian classification rules
Automatic tool for Alzheimer's disease diagnosis using PCA and Bayesian classification rules
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DOI：
10.1049/el.2009.0176
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发表时间：
2009-04-09
影响因子：
1.1
通讯作者：
Puntonet, C. G.
中科院分区：
文献类型：
--
作者：
Lopez, M.;Ramirez, J.;Puntonet, C. G.
展示了一种自动工具，可帮助解释单光子发射计算机断层扫描 (SPECT) 和正电子发射断层扫描 (PET) 图像，以诊断阿尔茨海默病 (AD)。要处理的主要问题是所谓的小尺寸样本，即与大量特征相比，可用图像数量较少。通过主成分分析（PCA）的方式集中降低特征空间的维数来解决这个问题。我们的方法基于贝叶斯分类器，它使用后验信息来确定对象属于哪个类别，SPECT 和 PET 图像的准确率分别为 88.6% 和 98.3%。这些结果意味着比其他现有技术所达到的准确度值有所提高。
An automatic tool to assist the interpretation of single photon emission computed tomography (SPECT) and positron emission tomography (PET) images for the diagnosis of the Alzheimer's disease (AD) is demonstrated. The main problem to be handled is the so-called small size sample, which consists of having a small number of available images compared to the large number of features. This problem is faced by intensively reducing the dimension of the feature space by means of principal component analysis (PCA). Our approach is based on Bayesian classifiers, which uses a posteriori information to determine in which class the subject belongs, yielding 88.6 and 98.3% accuracy values for SPECT and PET images, respectively. These results mean an improvement over the accuracy values reached by other existing techniques.