Liu Luyan, Wang Qian, Adeli Ehsan, Zhang Lichi, Zhang Han, Shen Dinggang
School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
Department of Radiology BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2016 Oct;9901:1-8. doi: 10.1007/978-3-319-46723-8_1. Epub 2016 Oct 2.
Parkinson's disease (PD) is a major progressive neurodegenerative disorder. Accurate diagnosis of PD is crucial to control the symptoms appropriately. However, its clinical diagnosis mostly relies on the subjective judgment of physicians and the clinical symptoms that often appear late. Recent neuroimaging techniques, along with machine learning methods, provide alternative solutions for PD screening. In this paper, we propose a novel feature selection technique, based on iterative canonical correlation analysis (ICCA), to investigate the roles of different brain regions in PD through T1-weighted MR images. First of all, and tissue volumes in brain regions of interest are extracted as two feature vectors. Then, a small group of significant features were selected using the iterative structure of our proposed ICCA framework from both feature vectors. Finally, the selected features are used to build a robust classifier for automatic diagnosis of PD. Experimental results show that the proposed feature selection method results in better diagnosis accuracy, compared to the baseline and state-of-the-art methods.
帕金森病（PD）是一种主要的进行性神经退行性疾病。准确诊断帕金森病对于适当控制症状至关重要。然而，其临床诊断大多依赖于医生的主观判断以及往往出现较晚的临床症状。近期的神经成像技术与机器学习方法一起，为帕金森病筛查提供了替代解决方案。在本文中，我们提出一种基于迭代典型相关分析（ICCA）的新型特征选择技术，以通过T1加权磁共振图像研究不同脑区在帕金森病中的作用。首先，提取感兴趣脑区中的[具体脑区1]和[具体脑区2]组织体积作为两个特征向量。然后，使用我们提出的ICCA框架的迭代结构从两个特征向量中选择一小部分显著特征。最后，将所选特征用于构建用于帕金森病自动诊断的稳健分类器。实验结果表明，与基线方法和现有技术方法相比，所提出的特征选择方法具有更高的诊断准确率。
