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Learning and Combining Image Similarities for Neonatal Brain Population Studies

  • Veronika A. ZimmerEmail author
  • Ben Glocker
  • Paul Aljabar
  • Serena J. Counsell
  • Mary A. Rutherford
  • A. David Edwards
  • Jo V. Hajnal
  • Miguel Ángel González Ballester
  • Daniel Rueckert
  • Gemma Piella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

Abstract

The characterization of neurodevelopment is challenging due to the complex structural changes of the brain in early childhood. To analyze the changes in a population across time and to relate them with clinical information, manifold learning techniques can be applied. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure in the embedding and highly application dependent. It has been shown that the combination of several notions of similarity and features can improve the new representation. However, how to combine and weight different similarites and features is non-trivial. In this work, we propose to learn the neighborhood structure and similarity measure used for manifold learning through Neighborhood Approximation Forests (NAFs). The recently proposed NAFs learn a neighborhood structure in a dataset based on a user-defined distance. A characterization of image similarity using NAFs enables us to construct manifold representations based on a previously defined criterion to improve predictions regarding structural and clinical information. In particular, NAFs can be used naturally to combine the affinities learned from multiple distances in a joint manifold towards a more meaningful representation and an improved characterization of the resulting embedding. We demonstrate the utility of NAFs in manifold learning on a population of preterm and in term neonates for classification regarding structural volume and clinical information.

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© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Veronika A. Zimmer
    • 1
    Email author
  • Ben Glocker
    • 3
  • Paul Aljabar
    • 4
  • Serena J. Counsell
    • 4
  • Mary A. Rutherford
    • 4
  • A. David Edwards
    • 4
  • Jo V. Hajnal
    • 4
  • Miguel Ángel González Ballester
    • 1
    • 2
  • Daniel Rueckert
    • 3
  • Gemma Piella
    • 1
  1. 1.SIMBioSys GroupUniversitat Pompeu FabraBarcelonaSpain
  2. 2.ICREABarcelonaSpain
  3. 3.Biomedical Image Analysis GroupImperial College LondonLondonUK
  4. 4.Imaging Sciences & Biomedical EngineeringKing’s College LondonLondonUK

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