基于sMRI的阿尔茨海默症分类影响因素研究  

Study on factors affecting classification of Alzheimer's disease based on structural MRI

作　　者：黎建忠 曾安[1,2] 潘丹 Song Xiaowei[5] 郭慧 王卓薇 LI Jianzhong;ZENG An;PAN Dan;SONG Xiaowei;GUO Hui;WANG Zhuowei(Faculty of Computer Science,Guangdong University of Technology,Guangzhou 510006,China;Guangdong Key Laboratory of Big Data Analysis and Processing,Guangzhou 510006;Modern Education Technical Center,Guangdong Construction Polytechnic,Guangzhou 510440;Guangzhou Benzhen Network Technology Co.Ltd.,Guangzhou 510095;ImageTech Lab,Simon Fraser University,Vamu：ouver V6B 5K3,Canada;Department of Medical Imaging,General Hospital of Tianjin Medical University,Tianfin 300052,China)

机构地区：[1]广东工业大学计算机学院,中国广州510006 [2]广东省大数据分析与处理重点实验室,中国广州510006 [3]广东建设职业技术学院现代教育技术中心,中国广州510440 [4]广州市本真网络科技有限公司,中国广州510095 [5]西蒙弗雷泽大学影像技术实验室 [6]天津医科大学总医院医学影像科,中国天津300052

出　　处：《生物医学工程研究》2018年第2期177-181,共5页Journal Of Biomedical Engineering Research

基　　金：国家自然科学基金资助项目(61772143;61300107;61672168);广东省自然科学基金资助项目(S2012010010212);广东省大数据分析与处理重点实验室开放基金资助项目(201801);广州市科技计划资助项目(201601010034;201804010278)

摘　　要：本研究提出基于三类解剖特征的SVM建模方法,探索样本、特征及算法选择三个因素,对阿尔茨海默症(AD)及其前驱阶段分类的重要性。该方法以三维重构s MRI后不同大脑区域的灰质体积、皮层表面积及其平均厚度三类特征作为SVM模型的输入参数,并采用十折交叉验证方法对AD患者、轻度认知损害患者和健康者进行分类识别,并与其他文献结果进行比较分析。实验结果表明,为了达到更高的分类准确率,选择合适的样本和特征,比选择算法更重要。此结论为未来AD的计算机辅助诊断研究工作提供了有益的指导。Aiming at the problem of classifying Alzheimer ＇s disease（ AD） and its prodromal stage,the factors such as training sample selection,feature extraction and classification algorithm selection were studied to exhibit their importance in improving the classification accuracy. Support vector machine（ SVM） modeling method based on three types of anatomical features was proposed. Three types of anatomical features（ the volume of gray matter,the surface area and the average thickness of the cerebral cortex） in different brain regions were extracted by 3 D reconstruction of s MRI images and were utilized to build a SVM model. With the help of 10-fold cross validation,the trained SVM model was employed to classify AD patients,patients with mild cognitive impairment（ MCI） and healthy subjects（ HC）. Compared with other classification results based on different data sets and different features published in other research papers,the experimental results in this study exhibite that it is more important to select the appropriate training data sets andfeatures than to select the classification algorithm. This conclusion might be helpful for the further research on the computer-aided diagnosis of AD.

关 键 词：阿尔茨海默症 轻度认知损害 结构化磁共振图像 三维重构 支持向量机 

分 类 号：R318[医药卫生—生物医学工程] TP391[医药卫生—基础医学]