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T-test based Alzheimer’s disease diagnosis with multi-feature in MRIs

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Abstract

Diagnosing Alzheimer’s disease (AD) with magnetic resonance imaging (MRI) has attracted increasing attention. In this paper, we propose a new feature selection method for AD diagnosis by selecting interested structures in brain MRI. In the proposed method, P-Value is used to obtain the independent principal features, and the structures that have large values are selected as interested structures. P-Value for every voxel is calculated by T-test between different image classes, then the average P-Value for every brain tissue is obtained. After these operations, we firstly use Statistical Parametric Mapping (SPM) software to pre-process MRI, secondly select interested structures based on T-test, then extract different texture characteristics as multi-feature, finally classify the images to diagnose AD by collaborative representation based classification (CRC). Extensive experiments were conducted to evaluate the proposed method, and the comparison results indicate that it achieves better performance in contrast with several existing algorithms.

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References

  1. Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S et al (2011) Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 56(2):766–781

    Article  Google Scholar 

  2. Feng L, Wee CY, Chen H et al (2013) Inter-Modality Relationship Constrained Multi-modality Multi-Task Feature Selection for Alzheimer's Disease and Mild Cognitive Impairment Identification. NeuroImage 84:466–475

    Google Scholar 

  3. Glenn F, Jonathan S (2007) SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information. Knowl Inf Syst 11(2):243–258

    Article  Google Scholar 

  4. Gray KR, Aljabar P, Heckemann RA et al (2012) Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. NeuroImage 65:167–175

    Article  Google Scholar 

  5. Hinrichs C, Singh V, Xu G et al (2011) Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population. NeuroImage 55(2):574–589

    Article  Google Scholar 

  6. Jin L, Wang J, Hu B, Wu F-X, Pan Y (2017) Alzheimer's Disease Classification Based on Individual Hierarchical Networks Constructed With 3-D Texture Features. IEEE Trans Nanobioscience 16(6):428–437

    Article  Google Scholar 

  7. Ketil O, Kjersti E, Trygve E, Mona B, Dag A (2017) Classifying Alzheimer’s disease, Lewy body dementia, and normal controls using 3D texture analysis in magnetic resonance images. Biomed Signal Process Control 33:19–29

    Article  Google Scholar 

  8. Liu Z, Xu T, Ma C, Gao C, Yang H (2017) Alzheimer’s disease diagnosis via interested structure selection in MRIs. In: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2017), 29–31 July 2017, Guilin, China

  9. Liu M, Zhang J, Yap P-T, Shen D (2017) View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data. Med Image Anal 36:123–134

    Article  Google Scholar 

  10. Ma X, Li Z, Jing B, Liu H, Li D et al (2016) Identify the Atrophy of Alzheimer's Disease, Mild Cognitive Impairment and Normal Aging Using Morphometric MRI Analysis. Front Aging Neurosci 8(27):243

    Google Scholar 

  11. Magnin B, Mesrob L, Kinkingnéhun S et al (2009) Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI. Neuroradiology 51(2):73–83

    Article  Google Scholar 

  12. Mesrob L, Magnin B, Colliot O et al (2008) Identification of Atrophy Patterns in Alzheimer’s Disease Based on SVM Feature Selection and Anatomical Parcellation. Medical Imaging and Augmented Reality 5128:124–132

    Article  Google Scholar 

  13. Shi Y, Suk H I, Gao Y et al (2014) Joint coupled-feature representation and coupled boosting for AD diagnosis. Proceedings of the 2014 I.E. Conference on Computer Vision and Pattern Recognition 2014:2721–2728

  14. Tong Y, Udupa JK, Sin S, Liu Z, Wileyto EP, Torigian DA, Arens R (2016) MR Image Analytics to Characterize the Upper Airway Structure in Obese Children with Obstructive Sleep Apnea Syndrome. PLoS One 11(8):e0159327

    Article  Google Scholar 

  15. Vemuri P, Gunter J, Senjem M et al (2008) Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. NeuroImage 39(3):1186–1197

    Article  Google Scholar 

  16. Xin LI, Tong LZ, Zhou XX et al (2011) Classification of 3D texture features based on MR image in discrimination of Alzheimer disease and mild cognitive impairment from normal controls. Chin J Med Imag Technol 27(5):1047–1051

    Google Scholar 

  17. Zhang L, Yang M, Feng X (2012) Sparse representation or collaborative representation: Which helps face recognition. IEEE Int Conf Comput Vis 2011(5):471–478

    Google Scholar 

  18. Zhu X, Yu H, Zhao K et al (2013) Classification method of mild Alzheimer's disease based on MR and PET imaging. Journal of Electronic Measurement & Instrument 27(9):850–858

    Article  Google Scholar 

  19. Zhu X, Lei Z, Zi H (2014) A sparse embedding and least variance encoding approach to hashing. IEEE Trans Image Process 23(9):3737

    Article  MathSciNet  Google Scholar 

  20. Zhu X, Xuelong L, Shichao Z (2016) Block-row sparse multiview multilabel learning for image classification. IEEE Transactions on Cybernetics 46(2):450

    Article  Google Scholar 

  21. Zhu X, Suk H-I, Wang L, Lee S-W, Shen D (2017) A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med Image Anal 75(6):570–577

    Google Scholar 

  22. Zhu X, Xuelong L, Shichao Z, Chunhua J, Wu X (2017) Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Transactions on Neural Networks and Learning Systems 28(6):1263–1275

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The authors would like to thank the anonymous reviewers and the associate editor for helpful comments and suggestions. Our study was funded by the National Natural Science Foundation of China (Grant No. 61562013), Natural Science Foundation of Guangxi Province (Grant No. 2017GXNSFDA198025), Innovation Project of GUET Graduate Education, the study abroad program for graduate student of Guilin University of Electronic Technology, the project of cultivating excellent degree papers for graduate students of GUET. Meanwhile, we want to thank the help of Huanhuan Ji, Zimin Wang, Qijia He, Rushi Lan on revising manuscript.

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Authors and Affiliations

  1. Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics, Guilin University of Electronic Technology, No. 1 Jinji Road, Qixing Strict, Guilin, 541000, China

    Zhenbing Liu, Tao Xu, Chao Ma, Chunyang Gao & Huihua Yang

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  1. Zhenbing Liu
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  2. Tao Xu
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  3. Chao Ma
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  4. Chunyang Gao
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Correspondence to Zhenbing Liu.

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Liu, Z., Xu, T., Ma, C. et al. T-test based Alzheimer’s disease diagnosis with multi-feature in MRIs. Multimed Tools Appl 77, 29687–29703 (2018). https://doi.org/10.1007/s11042-018-5768-0

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  • DOI: https://doi.org/10.1007/s11042-018-5768-0

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