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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 75–82Cite as

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Neighbourhood Approximation Forests

Neighbourhood Approximation Forests

  • Ender Konukoglu19,
  • Ben Glocker19,
  • Darko Zikic19 &
  • …
  • Antonio Criminisi19 
  • Conference paper
  • 4250 Accesses

  • 13 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7512)

Abstract

Methods that leverage neighbourhood structures in high-dimensional image spaces have recently attracted attention. These approaches extract information from a new image using its “neighbours” in the image space equipped with an application-specific distance. Finding the neighbourhood of a given image is challenging due to large dataset sizes and costly distance evaluations. Furthermore, automatic neighbourhood search for a new image is currently not possible when the distance is based on ground truth annotations. In this article we present a general and efficient solution to these problems. “Neighbourhood Approximation Forests” (NAF) is a supervised learning algorithm that approximates the neighbourhood structure resulting from an arbitrary distance. As NAF uses only image intensities to infer neighbours it can also be applied to distances based on ground truth annotations. We demonstrate NAF in two scenarios: i) choosing neighbours with respect to a deformation-based distance, and ii) age prediction from brain MRI. The experiments show NAF’s approximation quality, computational advantages and use in different contexts.

Keywords

  • Training Image
  • Image Space
  • Supervise Learning Algorithm
  • Binary Test
  • Binary Decision Tree

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Microsoft Research Cambridge, UK

    Ender Konukoglu, Ben Glocker, Darko Zikic & Antonio Criminisi

Authors
  1. Ender Konukoglu
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  2. Ben Glocker
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  3. Darko Zikic
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  4. Antonio Criminisi
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Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Konukoglu, E., Glocker, B., Zikic, D., Criminisi, A. (2012). Neighbourhood Approximation Forests. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_10

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  • DOI: https://doi.org/10.1007/978-3-642-33454-2_10

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