东南大学学报（自然科学版）2016，Vol.46Issue(2)：271-276,6.DOI:10.3969/j.issn.1001-0505.2016.02.008
基于磁共振影像特征集成融合的 AD 诊断
AD diagnosis based on integrated fusion of MR image features
摘要
Abstract
In order to obtain higher and more stable diagnostic accuracy of Alzheimer's disease (AD), the texture features of magnetic resonance(MR) images were integrated and fused for AD diagnosis.First, the volume and texture features of the left and right parts of multiple anatomical structures were extracted based on pathological knowledge.Secondly, by combining the chain-like agent genetic algorithm ( CAGA) and support vector machine ( SVM) , a feature selection classifica-tion ensemble model was designed to conduct deep feature selection and realize feature fusion.Final-ly, the fused features were used for classification and diagnosis of AD and the classification results are compared with those before fusion and those obtained by the p-value method.The experimental results show that the features fused by this proposed algorithm have higher and more stable classifica-tion accuracy, sensitivity and specificity than the features before fusion and the features selected by the p-value method.关键词
磁共振影像/阿尔茨海默病/影像特征融合/特征选择分类集成模型/链式智能体遗传算法/支持向量机Key words
magnetic resonance (MR) image/Alzheimer's disease (AD)/image feature fusion/feature selection classification ensemble model/chain-like agent genetic algorithm(CA-GA)/support vector machine(SVM)分类
医药卫生引用本文复制引用
李勇明,吕洋,李帆,王品,邱明国,刘书君,闫瑾..基于磁共振影像特征集成融合的 AD 诊断[J].东南大学学报（自然科学版）,2016,46(2):271-276,6.基金项目
国家自然科学基金资助项目（61108086，91438104，11304382）、中国博士后科学基金资助项目（2013M532153）、中央高校基本科研业务费专项资金资助项目（CDJZR12160011，CDJZR13160008，CDJZR155507）、重庆市博士后科研项目特别资助项目． （）
