Combining Morphological Information in a Manifold Learning Framework: Application to Neonatal MRI

P. Aljabar*, R. Wolz, L. Srinivasan, S. Counsell, J. P. Boardman, M. Murgasova, V. Doria, M. A. Rutherford, A. D. Edwards, J. V. Hajnal, D. Rueckert

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

MR, image data can provide many features or measures although any single measure is unlikely to comprehensively characterize the underlying morphology. We present a framework in which multiple measures are used in manifold learning steps to generate coordinate embeddings which are then combined to give an improved single representation of the population. An application to neonatal brain MRI data shows that the use of shape and appearance measures in particular leads to biologically plausible and consistent representations correlating well with clinical data. Orthogonality among the correlations suggests the embedding components relate to comparatively independent morphological features. The rapid changes that occur in brain shape and in MR image appearance during neonatal brain development justify the use of shape measures (obtained from a deformation metric) and appearance measures (obtained from image similarity). The benefit; of combining separate embeddings is demonstrated by improved correlations with clinical data and we illustrate the potential of the proposed framework in characterizing trajectories of brain development.

Original languageEnglish
Title of host publicationMEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2010, PT III
EditorsT Jiang, N Navab, JPW Pluim, MA Viergever
PublisherSpringer-Verlag Berlin Heidelberg
Pages1-8
Number of pages8
ISBN (Print)978-3-642-15710-3
Publication statusPublished - 2010
Event13th International Conference on Medical Image Computing and Computer-Assisted Intervention - Beijing, United Kingdom
Duration: 20 Sep 201024 Sep 2010

Publication series

NameLecture Notes in Computer Science
PublisherSPRINGER-VERLAG BERLIN
Volume6363
ISSN (Print)0302-9743

Conference

Conference13th International Conference on Medical Image Computing and Computer-Assisted Intervention
CountryUnited Kingdom
Period20/09/1024/09/10

Keywords

  • DIMENSIONALITY REDUCTION
  • IMAGES
  • REGISTRATION
  • MORPHOMETRY

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