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).
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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.
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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
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DOI: https://doi.org/10.1007/978-3-319-47157-0_38
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