生物医学工程学杂志

生物医学工程学杂志

基于监督局部线性嵌入方法的阿尔茨海默病磁共振成像分类研究

查看全文

针对阿尔茨海默病(AD)早期阶段分类这一研究难题,传统的线性特征提取算法很难从其高维特征中挖掘出鉴别能力较强的信息来有效地表示样本特征。因此,本文采用监督局部线性嵌入(SLLE)特征提取算法,对 412 例受试者的大脑皮质厚度(CTH)和脑感兴趣区域体积(VOI)特征进行提取,减少其冗余特征以提高识别精度。受试者来源于阿尔茨海默病神经影像学(ADNI)数据集,包含 93 例稳定型轻度认知障碍(sMCI)、96 例遗忘型轻度认知障碍(aMCI)、86 例 AD 患者和 137 例认知正常对照老年人(CN)样本。本文采用的 SLLE 算法是通过添加距离修正项来计算每个样本点的近邻点,并用近邻点线性表示样本,得到局部重建权值矩阵,进而求出高维数据的低维映射。为验证该算法在分类识别中的有效性,本文将主成分分析(PCA)、近邻最小最大投影(NMMP)、局部线性映射(LLE)及 SLLE 等特征提取算法分别与支持向量机(SVM)分类器组合,对 CN 与 sMCI、CN 与 aMCI、CN 与 AD、sMCI 与 aMCI、sMCI 与 AD 和 aMCI 与 AD 六组实验数据进行分类识别。结果显示,以 VOI 为特征,利用 SLLE 和 SVM 的复合算法对 sMCI 和 aMCI 的分类准确度、灵敏度、特异性分别为 65.16%、63.33%、67.62%,基于 LLE 和 SVM 的复合算法分类结果分别为 64.08%、66.14%、62.77%,而基于传统 SVM 则分别为 57.25%、56.28%、58.08%。经比较,发现 SLLE 和 SVM 组合算法的识别精度较 LLE 和 SVM 的组合算法提高了 1.08%,较 SVM 提高了 7.91%。因此,利用 SLLE 和 SVM 这一复合算法进行分类识别更有利于 AD 的早期诊断。

In order to solve the problem of early classification of Alzheimer’s disease (AD), the conventional linear feature extraction algorithm is difficult to extract the most discriminative information from the high-dimensional features to effectively classify unlabeled samples. Therefore, in order to reduce the redundant features and improve the recognition accuracy, this paper used the supervised locally linear embedding (SLLE) algorithm to transform multivariate data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions. The 412 individuals were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) including stable mild cognitive impairment (sMCI, n = 93), amnestic mild cognitive impairment (aMCI, n = 96), AD (n = 86) and cognitive normal controls (CN, n = 137). The SLLE algorithm used in this paper is to calculate the nearest neighbors of each sample point by adding the distance correction term, and the locally linear reconstruction weight matrix was obtained from its nearest neighbors, then the low dimensional mapping of the high dimensional data can be calculated. In order to verify the validity of SLLE in the task of classification, the feature extraction algorithms such as principal component analysis (PCA), Neighborhood MinMax Projection (NMMP), locally linear mapping (LLE) and SLLE were respectively combined with support vector machines (SVM) classifier to obtain the accuracy of classification of CN and sMCI, CN and aMCI, CN and AD, sMCI and aMCI, sMCI and AD, and aMCI and AD, respectively. Experimental results showed that our method had improvements (accuracy/sensitivity/specificity: 65.16%/63.33%/67.62%) on the classification of sMCI and aMCI by comparing with the combination algorithm of LLE and SVM (accuracy/sensitivity/specificity: 64.08%/66.14%/62.77%) and SVM (accuracy/sensitivity/specificity: 57.25%/56.28%/58.08%). In detail the accuracy of the combination algorithm of SLLE and SVM is 1.08% higher than the combination algorithm of LLE and SVM, and 7.91% higher than SVM. Thus, the combination of SLLE and SVM is more effective in the early diagnosis of Alzheimer’s disease.

关键词: 阿尔茨海默病; 特征提取; 监督局部线性嵌入; 遗忘型轻度认知障碍

Key words: Alzheimer’s disease; feature extraction; supervised locally linear embedding; amnestic mild cognitive impairment

引用本文: 赵海峰, 葛园园, 王政. 基于监督局部线性嵌入方法的阿尔茨海默病磁共振成像分类研究. 生物医学工程学杂志, 2018, 35(4): 613-620. doi: 10.7507/1001-5515.201703002 复制

登录后 ,请手动点击刷新查看全文内容。 没有账号,
登录后 ,请手动点击刷新查看图表内容。 没有账号,
1. Alzheimer’s Association. 2015 Alzheimer’s disease facts and figures. Alzheimers Dement, 2015, 11(3): 332.
2. Weiner M W, Veitch D P, Aisen P S, et al; Alzheimer’s Disease Neuroimaging Initiative. 2014 update of the Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement, 2015, 11(6): e1-120.
3. Wyman B T, Harvey D J, Crawford K, et al. Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimers Dement, 2013, 9(3): 332-337.
4. Dickerson B C, Wolk D A; Alzheimer’s Disease Neuroimaging Initiative. MRI cortical thickness biomarker predicts AD-like CSF and cognitive decline in normal adults. Neurology, 2012, 78(2): 84-90.
5. Jolliffe I T. Principal component analysis and factor analysis. New York: Springer, 1986: 115-128.
6. Hyvärinen A, Hurri J, Hoyer P O. Independent component analysis. IEEE Transactions on Neural Networks, 2009, 15(2): 529.
7. Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(550): 2323-2326.
8. Liu Xin, Tosun D, Weiner M W, et al. Locally linear embedding (LLE) for MRI based Alzheimer’s disease classification. Neuroimage, 2013, 83: 148-157.
9. Luo Z, Zeng L L, Chen F. Classification of patients with Alzheimer’s disease based on structural MRI using locally linear embedding (LLE)// Chinese Conference on Biometric Recognition. Springer International Publishing, 2014: 535-540.
10. Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3, SI): 389-396.
11. Ridder D D, Kouropteva O, Okun O, et al. Supervised locally linear embedding// Artificial Neural Networks and Neural Information Processing—ICANN/ICONIP 2003. Springer Berlin Heidelberg, 2003: 333-341.
12. Nie F, Xiang S, Zhang C. Neighborhood MinMax projections// International Joint Conference on Artificial Intelligence. Morgan Kaufmann Publishers Inc, 2007: 993-998.
13. Khedher L, Ramírez J, Górriz J M, et al. Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing, 2015, 151(1): 139-150.
14. 管杰, 杨文璐. 基于独立成分分析的双模态影像分类研究. 生物医学工程学杂志, 2014, 31(5): 1031-1036.
15. Bruce F. FreeSurfer. Neuroimage, 2012, 62(2): 774-781.
16. 谢兵, 王健, 黎川, 等. FreeSurfer图像分析软件在视觉功能磁共振研究中的运用// 2010 中华医学会影像技术分会第十八次全国学术大会论文集. 乌鲁木齐: 中华医学会, 2010: 88-89.
17. Beheshti I, Demirel H; Alzheimer’s Disease Neuroimaging Initiative. Feature-ranking-based Alzheimer’s disease classification from structural MRI. Magn Reson Imaging, 2016, 34(3): 252-263.
18. Suk H I, Shen D. Deep learning-based feature representation for AD/MCI classification// Medical Image Computing & Computer-assisted Intervention: MICCAI International Conference on Medical Image Computing & Computer-assisted Intervention. Berlin, Heidelberg: Springer, 2013: 583.
19. Xu Lele, Wu Xia, Chen Kewei, et al. Multi-modality sparse representation-based classification for Alzheimer’s disease and mild cognitive impairment. Comput Methods Programs Biomed, 2015, 122(2): 182-190.