Chapter title |
Fast Neuroimaging-Based Retrieval for Alzheimer’s Disease Analysis
|
---|---|
Chapter number | 38 |
Book title |
Machine Learning in Medical Imaging
|
Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-47157-0_38 |
Pubmed ID | |
Book ISBNs |
978-3-31-947156-3, 978-3-31-947157-0
|
Authors |
Xiaofeng Zhu, Kim-Han Thung, Jun Zhang, Dinggang Shen, Zhu, Xiaofeng, Thung, Kim-Han, Zhang, Jun, Shen, Dinggang |
Editors |
Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk |
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). |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 2 | 22% |
Student > Master | 2 | 22% |
Student > Doctoral Student | 1 | 11% |
Lecturer | 1 | 11% |
Researcher | 1 | 11% |
Other | 0 | 0% |
Unknown | 2 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 3 | 33% |
Mathematics | 1 | 11% |
Engineering | 1 | 11% |
Unknown | 4 | 44% |