Manifold Learning Combining Imaging with Non-Imaging Information

Robin Wolz, Paul Aljabar, Joseph Hajnal, Jyrki Lötjönen, Daniel Rueckert

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

6 Citations (Scopus)


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 languageEnglish
Title of host publication2011 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
PublisherIEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)978-1-4244-4128-0
ISBN (Print)978-1-4244-4127-3
Publication statusPublished - 2011
MoE publication typeA4 Article in a conference publication
Event8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011 - Chicago, IL, United States
Duration: 30 Mar 20112 Apr 2011


Conference8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011
Abbreviated titleISBI 2011
Country/TerritoryUnited States
CityChicago, IL


  • classification
  • brain


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