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首页> 外文期刊>Neuroscience Letters: An International Multidisciplinary Journal Devoted to the Rapid Publication of Basic Research in the Brain Sciences >Analysis of SPECT brain images for the diagnosis of Alzheimer's disease using moments and support vector machines.
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Analysis of SPECT brain images for the diagnosis of Alzheimer's disease using moments and support vector machines.

机译：使用矩和支持向量机对SPECT脑图像进行分析以诊断阿尔茨海默氏病。

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摘要

This paper presents a computer-aided diagnosis technique for improving the accuracy of diagnosing the Alzheimer's type dementia. The proposed methodology is based on the calculation of the skewness for each m-by-m-by-m sliding block of the SPECT brain images. The center pixel in this m-by-m-by-m block is replaced by the skewness value to build a new 3-D brain image which is used for classification purposes. After that, voxels which present a Welch's t-statistic between classes, Normal and Alzheimer's images, higher (or lower) than a threshold are selected. The mean, standard deviation, skewness and kurtosis are calculated for these selected voxels and they are subjected as features to linear kernel based support vector machine classifier. The proposed methodology reaches accuracy higher than 99% in the classification task.
机译：本文提出了一种计算机辅助诊断技术，以提高诊断阿尔茨海默氏型痴呆症的准确性。所提出的方法是基于对SPECT脑图像的每个m×m×m滑动块的偏度的计算。该m×m×m块中的中心像素被偏度值替换，以构建新的3-D脑图像，用于分类目的。之后，选择在类（正常图像和阿尔茨海默氏图像）之间高于阈值（或低于阈值）的Welch t统计量的体素。为这些选定的体素计算平均值，标准差，偏度和峰度，并将它们作为基于线性核的支持向量机分类器的特征。所提出的方法在分类任务中达到的准确度高于99％。

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