摘要
在这项工作中,我们提出了一个早期诊断阿尔茨海默氏症的全自动的计算机辅助诊断方法。我们研究了类别(标为正常对照组和可能的阿尔茨海默病)之间的距离,使用魏氏t检验计算大脑116个区域。我们对选择的魏氏t检验值最高值的区域为特征进行分类。此外,我们还根据t检验(最低的t检验绝对值),研究了少有差别的区域,以它们作为参考。我们显示根据魏氏t检验,少有差别和最有差别的两个区域的强度值的平均值和标准差,可以合并为一个向量。该向量的系数和相位显示组间的统计上差异,可用于改进分类的工作。我们展示了它们如何可以应用输入到支持向量机分类器。用被选定的三维像素的宽范围内产出精度高达91.5%的70张 SPECT脑图像进入SPECT脑数据库测试了所提出的方法。
关键词: 计算机辅助诊断、特征提取与选择、SPECT脑显像,支持向量机。
Current Alzheimer Research
Title:An Optimal Approach for Selecting Discriminant Regions for the Diagnosis of Alzheimer's Disease
Volume: 13 Issue: 7
Author(s): D. Salas-Gonzalez, F. Segovia, F. J. Martínez-Murcia, E. W. Lang, J. M. Gorriz, J. Ramırez
Affiliation:
关键词: 计算机辅助诊断、特征提取与选择、SPECT脑显像,支持向量机。
摘要: In this work, we present a fully automatic computer-aided diagnosis method for the early diagnosis of the Alzheimer’s disease. We study the distance between classes (labelled as normal controls and possible Alzheimer’s disease) calculated in 116 regions of the brain using the Welchs’s t-test. We select the regions with highest Welchs’s t-test value as features to perform classification. Furthermore, we also study the less discriminative region according to the t-test (regions with lowest t-test absolute values) in order to use them as reference. We show that the mean and standard deviation of the intensity values in these two regions, the less and most discriminative according to the Welch’s ttest, can be combined as a vector. The modulus and phase of this vector reveal statistical differences between groups which can be used to improve the classification task. We show how they can be used as input for a support vector machine classifier. The proposed methodology is tested in a SPECT brain database of 70 SPECT brain images yielding an accuracy up to 91.5% for a wide range of selected voxels.
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Cite this article as:
D. Salas-Gonzalez, F. Segovia, F. J. Martínez-Murcia, E. W. Lang, J. M. Gorriz, J. Ramırez , An Optimal Approach for Selecting Discriminant Regions for the Diagnosis of Alzheimer's Disease, Current Alzheimer Research 2016; 13 (7) . https://dx.doi.org/10.2174/1567205013666160415154852
DOI https://dx.doi.org/10.2174/1567205013666160415154852 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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