Supervised locally linear embedding for magnetic resonance imaging based Alzheimer’s disease classification_Journal of Biomedical Engineering

Authors：

ZHAO Haifeng ,  GE Yuanyuan , WANG Zheng

Key Lab of Intelligent Computing and Signal Processing of MOE & School of Computer and Technology, Anhui University, Hefei 230601, P.R.China;

Corresponding author：

GE Yuanyuan, Email: gehanna@163.com

Keywords：

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

DOI：

10.7507/1001-5515.201703002

Video：

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Abstract Full text Figures/Tables Video References Cited by

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.

Citation： ZHAO Haifeng, GE Yuanyuan, WANG Zheng. Supervised locally linear embedding for magnetic resonance imaging based Alzheimer’s disease classification. Journal of Biomedical Engineering, 2018, 35(4): 613-620. doi: 10.7507/1001-5515.201703002 Copy

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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.

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.

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