Depression discrimination using fMRI and DTI data by wavelet based fusion scheme  

基于fMRI和DTI小波融合信号的抑郁症识别(英文)

作　　者：赵竟 罗国平[1] 姚志剑[2] 卢青[1] 

机构地区：[1]东南大学学习科学研究中心,南京210096 [2]南京医科大学附属脑科医院,南京210029

出　　处：《Journal of Southeast University(English Edition)》2012年第1期25-28,共4页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.30900356,81071135)

摘　　要：Both functional magnetic resonance imaging （fMRI） and diffusion tensor imaging （DTI） can provide different information of the human brain, so using the wavelet transform method can achieve a fusion of these two types of image data and can effectively improve the depression recognition accuracy. Multi-resolution wavelet decomposition is used to transform each type of images to the frequency domain in order to obtain the frequency components of the images. To each subject, decomposition components of two images are then added up separately according to their frequencies. The inverse discrete wavelet transform is used to reconstruct the fused images. After that, principal component analysis （PCA） is applied to reduce the dimension and obtain the features of the fusion data before classification. Based on the features of the fused images, an accuracy rate of 80. 95 % for depression recognition is achieved using a leave-one-out cross-validation test. It can be concluded that this wavelet fusion scheme has the ability to improve the current diagnosis of depression.功能核磁共振和弥散张量这2种成像方式能够反应人类大脑不同方面的信息,采用小波变换的方法来对这2种医学图像进行融合可以有效改善抑郁症的识别准确率.首先,利用多尺度小波分解方法把每种类型的图像都转换到频域,以得到各频率的成分参数.其次,对于每个被试,将2种图像的分解参数根据频率各自相加,并且通过小波逆变换重建出融合图像.然后,使用主成分分析方法对融合的数据进行降维并得到图像特征.基于融合后图像的特征,采用留一检验方法最终得到了80.95%的抑郁症识别率.可以看出,该小波融合方法能够对当前抑郁症的诊断识别进行有效的改进.

关 键 词：classification functional magnetic resonanceimaging （fMRI） diffusion tensor imaging （DTI） medicalimage fusion DEPRESSION 

分 类 号：Q64[生物学—生物物理学] TP310.4[自动化与计算机技术—计算机软件与理论]