Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI
Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI
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DOI：
10.1007/s00234-008-0463-x
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发表时间：
2009-02-01
期刊：
影响因子：
2.8
通讯作者：
Benali, Habib
中科院分区：
文献类型：
--
作者：
Magnin, Benoit;Mesrob, Lilia;Benali, Habib
研究了16例阿尔茨海默病患者[平均年龄+/-标准差(SD)=74.1+/-5.2岁，简易智力评分检查(MMSE)=23.1+/-2.9]和22例老年对照组(72.3+/-5.0岁，MMSE=28.5+/-1.3)。每个受试者的三维T1加权MR图像被自动分割成感兴趣区域(ROI)。基于每个感兴趣区域提取的灰质特征，采用支持向量机算法对AD患者进行分类，并采用基于Bootstrap重采样的统计方法确保分类结果的稳健性。AD患者和对照组的平均正确分类结果达到94.5%(平均特异度为96.6%；平均敏感度为91.5%)。该方法具有区分AD患者和老年对照组的潜力，从而有助于AD的早期诊断。
We present and evaluate a new automated method based on support vector machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to discriminate between patients with Alzheimer's disease (AD) and elderly control subjects.We studied 16 patients with AD [mean age +/- standard deviation (SD) = 74.1 +/- 5.2 years, mini-mental score examination (MMSE) = 23.1 +/- 2.9] and 22 elderly controls (72.3 +/- 5.0 years, MMSE = 28.5 +/- 1.3). Three-dimensional T1-weighted MR images of each subject were automatically parcellated into regions of interest (ROIs). Based upon the characteristics of gray matter extracted from each ROI, we used an SVM algorithm to classify the subjects and statistical procedures based on bootstrap resampling to ensure the robustness of the results.We obtained 94.5% mean correct classification for AD and control subjects (mean specificity, 96.6%; mean sensitivity, 91.5%).Our method has the potential in distinguishing patients with AD from elderly controls and therefore may help in the early diagnosis of AD.