Abstract
Multi-atlas based methods using magnetic resonance imaging (MRI) have been recently proposed for automatic diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, most existing multi-atlas based methods simply average or concatenate features generated from multiple atlases, which ignores the important underlying structure information of multi-atlas data. In this paper, we propose a novel relationship induced multi-atlas learning (RIML) method for AD/MCI classification. Specifically, we first register each brain image onto multiple selected atlases separately, through which multiple sets of feature representations can be extracted. To exploit the structure information of data, we develop a relationship induced sparse feature selection method, by employing two regularization terms to model the relationships among atlases and among subjects. Finally, we learn a classifier based on selected features in each atlas space, followed by an ensemble classification strategy to combine multiple classifiers for making a final decision. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrate that our method achieves significant performance improvement for AD/MCI classification, compared with several state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M.O., Chupin, M., Benali, H., Colliot, O., Initiative, A.D.N.: 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 (2011)
Wolz, R., Julkunen, V., Koikkalainen, J., Niskanen, E., Zhang, D.P., Rueckert, D., Soininen, H., Lötjönen, J., Alzheimer’s Disease Neuroimaging Initiative: Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PLOS ONE 6(10), e25446 (2011)
Koikkalainen, J., Lötjönen, J., Thurfjell, L., Rueckert, D., Waldemar, G., Soininen, H., Alzheimer’s Disease Neuroimaging Initiative: Multi-template tensor-based morphometry: application to analysis of Alzheimer’s disease. NeuroImage 56(3), 1134–1144 (2011)
Liu, M., Zhang, D., Shen, D.: View-centralized multi-atlas classification for Alzheimer’s disease diagnosis. Hum. Brain Mapp. 36, 1847–1865 (2015)
Liu, M., Zhang, D.: Pairwise constraint-guided sparse learning for feature selection. IEEE Transactions on Cybernetics 46, 298–310 (2015)
Liu, M., Zhang, D., Chen, S., Xue, H.: Joint binary classifier learning for ECOC-based multi-class classification. IEEE Trans. Pattern Anal. Mach. Intell. (2015)
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)
Wang, Y., Nie, J., Yap, P.T., Li, G., Shi, F., Geng, X., Guo, L., Shen, D., Initiative, A.D.N.: Knowledge-guided robust MRI brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates. PLOS ONE 9(1), e77810 (2014)
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)
Fan, Y., Shen, D., Gur, R.C., Gur, R.E., Davatzikos, C.: COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Trans. Med. Imaging 26(1), 93–105 (2007)
Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143–156 (2001)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Shen, D., Davatzikos, C.: Hammer: hierarchical attribute matching mechanism for elastic registration. IEEE Trans. Med. Imaging 21(11), 1421–1439 (2002)
Goldszal, A.F., Davatzikos, C., Pham, D.L., Yan, M.X., Bryan, R.N., Resnick, S.M.: An image-processing system for qualitative and quantitative volumetric analysis of brain images. J. Comput. Assist. Tomogr. 22(5), 827–837 (1998)
Min, R., Wu, G., Cheng, J., Wang, Q., Shen, D.: Multi-atlas based representations for Alzheimer’s disease diagnosis. Hum. Brain Mapp. 35(10), 5052–5070 (2014)
Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 6, 583–598 (1991)
Jin, Y., Shi, Y., Zhan, L., Gutman, B.A., de Zubicaray, G.I., McMahon, K.L., Wright, M.J., Toga, A.W., Thompson, P.M.: Automatic clustering of white matter fibers in brain diffusion mri with an application to genetics. Neuroimage 100, 75–90 (2014)
Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3-d object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)
Ji, R., Gao, Y., Hong, R., Liu, Q., Tao, D., Li, X.: Spectral-spatial constraint hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 52(3), 1811–1824 (2014)
Liu, M., Miao, L., Zhang, D.: Two-stage cost-sensitive learning for software defect prediction. IEEE Trans. Reliab. 63(2), 676–686 (2014)
Nesterov, Y.: Smooth minimization of non-smooth functions. Math. Program. 103(1), 127–152 (2005)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodological) 58, 267–288 (1996)
Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619–1630 (2006)
Acknowledgements
This study was supported by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, and AG042599), the National Natural Science Foundation of China (Nos. 61422204, 61473149), the Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20130034), and the NUAA Fundamental Research Fund under grant number NE2013105.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Liu, M., Zhang, D., Adeli-Mosabbeb, E., Shen, D. (2016). Relationship Induced Multi-atlas Learning for Alzheimer’s Disease Diagnosis . In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2015. Lecture Notes in Computer Science(), vol 9601. Springer, Cham. https://doi.org/10.1007/978-3-319-42016-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-42016-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42015-8
Online ISBN: 978-3-319-42016-5
eBook Packages: Computer ScienceComputer Science (R0)