Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011 |
| 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 |
|---|---|
| Abbreviated title | ISBI 2011 |
| Country | United States |
| City | Chicago, IL |
| Period | 30/03/11 → 2/04/11 |
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Keywords
- classification
- brain
Cite this
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Manifold Learning Combining Imaging with Non-Imaging Information. / Wolz, Robin; Aljabar, Paul; Hajnal, Joseph; Lötjönen, Jyrki; Rueckert, Daniel.
Proceedings: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011. IEEE Institute of Electrical and Electronic Engineers , 2011. p. 1637-1640.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Manifold Learning Combining Imaging with Non-Imaging Information
AU - Wolz, Robin
AU - Aljabar, Paul
AU - Hajnal, Joseph
AU - Lötjönen, Jyrki
AU - Rueckert, Daniel
N1 - Project code: 18493
PY - 2011
Y1 - 2011
N2 - 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 A?42 and hippocampal volume as meta-information to achieve significantly improved classification results for subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
AB - 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 A?42 and hippocampal volume as meta-information to achieve significantly improved classification results for subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
KW - classification
KW - brain
U2 - 10.1109/ISBI.2011.5872717
DO - 10.1109/ISBI.2011.5872717
M3 - Conference article in proceedings
SN - 978-1-4244-4127-3
SP - 1637
EP - 1640
BT - Proceedings
PB - IEEE Institute of Electrical and Electronic Engineers
ER -