Abstract
Recent work suggests that the space of brain magnetic resonance (MR) images can be described by a nonlinear and low-dimensional manifold. In the context of classifying Alzheimer's disease (AD) patients from healthy controls, we propose a method to incorporate subject meta-information into the manifold learning step. Information such as gender, age or genotype is often available in clinical studies and can inform the classification of a given query subject. In the proposed method, such information, whether discrete or continuous, can be used as an additional input to manifold learning and to enrich a distance measure derived from pairwise image similarities. Building on previous work, the Laplacian eigenmap objective function is extended to include the additional information. We use the ApoE genotype, the CSF-concentration of Aβ42 and hippocampal volume as meta-information to achieve significantly improved classification results for subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
Original language | English |
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Title of host publication | 2011 IEEE International Symposium on Biomedical Imaging |
Subtitle of host publication | From Nano to Macro |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 1637-1640 |
ISBN (Electronic) | 978-1-4244-4128-0 |
ISBN (Print) | 978-1-4244-4127-3 |
DOIs | |
Publication status | Published - 2011 |
MoE publication type | A4 Article in a conference publication |
Event | 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011 - Chicago, IL, United States Duration: 30 Mar 2011 → 2 Apr 2011 |
Conference
Conference | 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011 |
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Abbreviated title | ISBI 2011 |
Country/Territory | United States |
City | Chicago, IL |
Period | 30/03/11 → 2/04/11 |
Keywords
- classification
- brain