[1]李勇明,李帆,朱雪茹,等.基于可分性距离判据和脑MR图像的AD症脑部年龄检测[J].东南大学学报(自然科学版),2016,46(6):1137-1142.[doi:10.3969/j.issn.1001-0505.2016.06.005]
 Li Yongming,Li Fan,Zhu Xueru,et al.Detection of brain age of Alzheimer’s disease based on separability distance criterion and MR image[J].Journal of Southeast University (Natural Science Edition),2016,46(6):1137-1142.[doi:10.3969/j.issn.1001-0505.2016.06.005]
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基于可分性距离判据和脑MR图像的AD症脑部年龄检测()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
46
期数:
2016年第6期
页码:
1137-1142
栏目:
计算机科学与工程
出版日期:
2016-11-20

文章信息/Info

Title:
Detection of brain age of Alzheimer’s disease based on separability distance criterion and MR image
作者:
李勇明12李帆1朱雪茹1王品1刘书君1邱明国2
1重庆大学通信工程学院, 重庆 400044; 2第三军医大学生物医学工程学院, 重庆 400038
Author(s):
Li Yongming12 Li Fan1 Zhu Xueru1 Wang Pin1 Liu Shujun1 Qiu Mingguo2
1College of Communication Engineering, Chongqing University, Chongqing 400044, China
2College of Biomedical Engineering, Third Military Medical University, Chongqing 400038, China
关键词:
脑部年龄检测 AD 分类 可分性距离判据 磁共振成像 支持向量回归机
Keywords:
brain age detection Alzheimer’s disease(AD) classification separability distance criterion magnetic resonance imaging(MRI) support vector regression(SVR)
分类号:
TP391
DOI:
10.3969/j.issn.1001-0505.2016.06.005
摘要:
阿尔茨海默病(AD)症不同状态所对应的脑部年龄与实际年龄存在偏差,为了准确预测该偏差得到脑部年龄,在现有基于脑磁共振(MR)图像年龄检测方法的基础上,提出了一种新的脑部年龄检测算法.首先,设定偏差搜索范围;然后,基于可分性距离判据设计了适应度函数,利用偏差和支持向量回归机(SVR)获得样本年龄估计值,并计算出其适应度值;接着,通过最大化适应度值获取偏差的最优估计,从而获得更有利于AD症分类的脑部年龄.与现有的年龄检测方法相比,对于正常对照组(NC)与AD患者、NC与轻度认知障碍患者(MCI)以及MCI与AD患者3种分类情况,所提方法的可分度值分别提高了0.178,0.033,0.017.因此,所提方法检测的年龄具有更好的可分性,更有利于提高AD症的分类准确率.
Abstract:
There is a deviation between brain age and actual age corresponding to different states of Alzheimer’s disease(AD). In order to accurately predict the deviation to obtain brain age, a new method of brain age detection is proposed based on the existing magnetic resonance(MR)image age detection methods. First, the deviation search range is set. Secondly, a fitness function is designed based on the distance separability criterion, and the brain age of samples is estimated via deviation and support vector regression(SVR)and the fitness value is calculated. Thirdly, the optimal deviation is obtained by maximizing the fitness value, so that obtaining brain ages more conducive to the classification of AD. Finally, the proposed method is compared with the existing age detection method. For three kinds of classification which are normal control group(NC)and AD,NC and mild cognitive impairment(MCI)as well as MCI and AD, based on the proposed algorithm, the separability can be improved by 0.178, 0.033, and 0.017, respectively. Therefore, the age detected with the proposed algorithm has better separability and helps to improve the classification accuracy of AD.

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备注/Memo

备注/Memo:
收稿日期: 2016-03-30.
作者简介: 李勇明(1976—),男,博士,副教授,yongmingli@cqu.edu.cn.
基金项目: 国家自然科学基金资助项目(61108086,91438104,11304382)、中央高校基本科研业务费专项资金资助项目(CDJZR155507,CDJZR12160011,CDJZR13160008)、中国博士后科学基金资助项目(2013M532153)、重庆市博士后科研项目特别资助项目、教育部留学回国人员基金资助项目.
引用本文: 李勇明,李帆,朱雪茹,等.基于可分性距离判据和脑MR图像的AD症脑部年龄检测[J].东南大学学报(自然科学版),2016,46(6):1137-1142. DOI:10.3969/j.issn.1001-0505.2016.06.005.
更新日期/Last Update: 2016-11-20