Large Deformation Diffeomorphic Metric Curve Mapping

  • Authors:

  • Joan Glaunès;Anqi Qiu;Michael I. Miller;Laurent Younes

  • Affiliations:

  • MAP5, CNRS UMR 8145, Université Paris Descartes, Paris, France 75006;Division of Bioengineering, National University of Singapore, Singapore, Singapore 117574;Center for Imaging Science, Johns Hopkins University, Baltimore, USA 21218;Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, USA 21218

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2008

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Abstract

We present a matching criterion for curves and integrate it into the large deformation diffeomorphic metric mapping (LDDMM) scheme for computing an optimal transformation between two curves embedded in Euclidean space 驴 d . Curves are first represented as vector-valued measures, which incorporate both location and the first order geometric structure of the curves. Then, a Hilbert space structure is imposed on the measures to build the norm for quantifying the closeness between two curves. We describe a discretized version of this, in which discrete sequences of points along the curve are represented by vector-valued functionals. This gives a convenient and practical way to define a matching functional for curves. We derive and implement the curve matching in the large deformation framework and demonstrate mapping results of curves in 驴2 and 驴3. Behaviors of the curve mapping are discussed using 2D curves. The applications to shape classification is shown and experiments with 3D curves extracted from brain cortical surfaces are presented.