Manifold Forests for Multi-modality Classification of Alzheimer’s Disease

  • K. R. Gray
  • P. Aljabar
  • R. A. Heckemann
  • A. Hammers
  • D. Rueckert
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Neurodegenerative disorders, such as Alzheimer’s disease, are associated with changes in multiple neuroimaging and biological measures. These may provide complementary information for diagnosis and prognosis. This chapter describes a framework within which a supervised version of manifold forests is used to perform multi-modality classification of patients with Alzheimer’s disease, patients with mild cognitive impairment, and elderly cognitively normal individuals. In this chapter, manifold forests are used to derive supervised similarity measures, with the aim of generating manifolds that are optimal for the task of clinical group discrimination. Embeddings are thus learned from labeled training data and used to infer the clinical labels of test data mapped into this space. Similarities from multiple (image- and non-image-based) modalities are combined to generate an embedding that simultaneously encodes information from all diverse features. Multi-modality classification is performed using coordinates from this joint embedding. Manifold forests provide consistent pairwise similarity measures for multiple modalities, thus facilitating the combination of different types of feature data.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • K. R. Gray
    • 1
  • P. Aljabar
    • 1
    • 2
  • R. A. Heckemann
    • 1
    • 3
  • A. Hammers
    • 1
    • 3
  • D. Rueckert
    • 1
  1. 1.Imperial College LondonLondonUK
  2. 2.King’s College LondonLondonUK
  3. 3.Fondation NeurodisLyonFrance

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