Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation
Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation
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DOI：
10.1016/j.neulet.2011.07.049
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发表时间：
2011-09-20
影响因子：
2.5
通讯作者：
Besga, A.
中科院分区：
文献类型：
--
作者：
Grana, M.;Termenon, M.;Besga, A.
本文的目的是从扩散张量成像(DTI)数据的两个标量度量--分数各向异性(FA)和平均扩散率(MD)中提取判别特征，并基于从FA或MD体积中提取的特征来训练和测试能够区分阿尔茨海默病(AD)患者和对照组的分类器。在本研究中，支持向量机分类器在FA和MD数据上进行了训练和测试。特征选择是通过计算对象间体素位置的FA或MD值与指定对象类别的指示性变量之间的皮尔逊相关性来完成的。选取绝对相关性较高的体素位置进行特征提取。结果是在圣地亚哥Apostol医院正在进行的一项研究中获得的，该研究收集了健康对照受试者和AD患者的解剖T1加权MRI卷和DTI数据。FA特征和线性支持向量机分类器在多个交叉验证研究中获得了良好的准确性、敏感性和特异性，支持了DTI衍生特征作为AD图像标记物的有效性以及基于这些特征构建AD计算机辅助诊断系统的可行性。(C)2011爱思唯尔爱尔兰有限公司。保留所有权利。
The aim of this paper is to obtain discriminant features from two scalar measures of Diffusion Tensor Imaging (DTI) data, Fractional Anisotropy (FA) and Mean Diffusivity (MD), and to train and test classifiers able to discriminate Alzheimer's Disease (AD) patients from controls on the basis of features extracted from the FA or MD volumes. In this study, support vector machine (SVM) classifier was trained and tested on FA and MD data. Feature selection is done computing the Pearson's correlation between FA or MD values at voxel site across subjects and the indicative variable specifying the subject class. Voxel sites with high absolute correlation are selected for feature extraction. Results are obtained over an on-going study in Hospital de Santiago Apostol collecting anatomical T1-weighted MRI volumes and DTI data from healthy control subjects and AD patients. FA features and a linear SVM classifier achieve perfect accuracy, sensitivity and specificity in several cross-validation studies, supporting the usefulness of DTI-derived features as an image-marker for AD and to the feasibility of building Computer Aided Diagnosis systems for AD based on them. (C) 2011 Elsevier Ireland Ltd. All rights reserved.