Fast Neuroimaging-Based Retrieval for Alzheimer’s Disease Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)


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).


Landmark Selection Alzheimer’s Disease Neuroimaging Initiative (ADNI) Nonlinear Registration Landmark Detection Short Binary Codes 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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