Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain.
Front Aging Neurosci. 2014 Feb 20;6:20. doi: 10.3389/fnagi.2014.00020. eCollection 2014.
Accurate identification of the most relevant brain regions linked to Alzheimer's disease (AD) is crucial in order to improve diagnosis techniques and to better understand this neurodegenerative process. For this purpose, statistical classification is suitable. In this work, a novel method based on support vector machine recursive feature elimination (SVM-RFE) is proposed to be applied on segmented brain MRI for detecting the most discriminant AD regions of interest (ROIs). The analyses are performed both on gray and white matter tissues, achieving up to 100% accuracy after classification and outperforming the results obtained by the standard t-test feature selection. The present method, applied on different subject sets, permits automatically determining high-resolution areas surrounding the hippocampal area without needing to divide the brain images according to any common template.
准确识别与阿尔茨海默病（AD）相关的最相关脑区对于改进诊断技术和更好地理解这种神经退行性过程至关重要。为此，统计分类是合适的。在这项工作中，提出了一种基于支持向量机递归特征消除（SVM-RFE）的新方法，用于对分割后的脑 MRI 进行检测，以找到最具判别力的 AD 感兴趣区（ROI）。分析同时在灰质和白质组织上进行，分类后准确率高达 100%，优于标准 t 检验特征选择的结果。本方法应用于不同的数据集，可以自动确定围绕海马区的高分辨率区域，而无需根据任何常见模板对脑图像进行划分。
