Abstract
This paper proposes a framework of fast neuroimaging-based retrieval and AD analysis, by three key steps: (1) landmark detection, which efficiently extracts landmark-based neuroimaging features without the need of nonlinear registration in testing stage; (2) landmark selection, which removes redundant/noisy landmarks via proposing a feature selection method that considers structural information among landmarks; and (3) hashing, which converts high-dimensional features of subjects into binary codes, for efficiently conducting approximate nearest neighbor search and diagnosis of AD. We have conducted experiments on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, and demonstrated that our framework could achieve higher performance than the comparison methods, in terms of accuracy and speed (at least 100 times faster).
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
Similar content being viewed by others
References
Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916–2929 (2013)
Huang, L., Jin, Y., Gao, Y., Thung, K., Shen, D., Alzheimer’s Disease Neuroimaging Initiative: Longitudinal clinical score prediction in Alzheimers disease with soft-split sparse regression based random forest. Neurobiol. Aging 46, 180–191 (2016)
Hutton, C., De Vita, E., Ashburner, J., Deichmann, R., Turner, R.: Voxel-based cortical thickness measurements in MRI. Neuroimage 40(4), 1701–1710 (2008)
Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: ICCV, pp. 2130–2137 (2009)
Liu, W., Wang, J., Kumar, S., Chang, S.F.: Hashing with graphs. In: ICML, pp. 1–8 (2011)
Shen, D., Davatzikos, C.: HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans. Med. Imaging 21(11), 1421–1439 (2002)
Thung, K., Wee, C., Yap, P., Shen, D.: Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. NeuroImage 91, 386–400 (2014)
Thung, K., Wee, C., Yap, P., Shen, D.: Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans. Brain Struct. Funct. 1–17 (2015)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NIPS, pp. 1753–1760 (2009)
Zhang, C., Qin, Y., Zhu, X., Zhang, J., Zhang, S.: Clustering-based missing value imputation for data preprocessing. In: INDIN, pp. 1081–1086 (2006)
Zhang, J., Gao, Y., Gao, Y., Munsell, B., Shen, D.: Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis. IEEE Trans. Med. Imaging PP(99), 1 (2016)
Zhang, J., Liang, J., Zhao, H.: Local energy pattern for texture classification using self-adaptive quantization thresholds. IEEE Trans. Image Process. 22(1), 31–42 (2013)
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)
Zhu, X., Huang, Z., Cheng, H., Cui, J., Shen, H.T.: Sparse hashing for fast multimedia search. ACM Trans. Inf. Syst. 31(2), 9 (2013)
Zhu, X., Huang, Z., Shen, H.T., Zhao, X.: Linear cross-modal hashing for efficient multimedia search. In: ACM Multimedia, pp. 143–152 (2013)
Zhu, X., Suk, H.-I., Shen, D.: A novel multi-relation regularization method for regression and classification in AD diagnosis. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 401–408. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10443-0_51
Zhu, X., Suk, H.I., Wang, L., Lee, S.W., Shen, D., et al.: A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med. Image Anal. (2015)
Zhu, X., Zhang, L., Huang, Z.: A sparse embedding and least variance encoding approach to hashing. IEEE Trans. Image Process. 23(9), 3737–3750 (2014)
Acknowledgements
This work was supported in part by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG042599). Xiaofeng Zhu was supported in part by the National Natural Science Foundation of China under grants 61573270 and 61263035.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Zhu, X., Thung, KH., Zhang, J., Shen, D. (2016). Fast Neuroimaging-Based Retrieval for Alzheimer’s Disease Analysis. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-47157-0_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47156-3
Online ISBN: 978-3-319-47157-0
eBook Packages: Computer ScienceComputer Science (R0)