一种鉴别诊断阿尔茨海默病的最佳方法

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.

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