Optimized Conformal Parameterization of Cortical Surfaces Using Shape Based Matching of Landmark Curves

  • Authors:

  • Lok Ming Lui;Sheshadri Thiruvenkadam;Yalin Wang;Tony Chan;Paul Thompson

  • Affiliations:

  • Department of Mathematics UCLA, , Los Angeles, CA 90095-1555;Department of Mathematics UCLA, , Los Angeles, CA 90095-1555;Department of Mathematics UCLA, , Los Angeles, CA 90095-1555 and Laboratory of Neuro Imaging and Brain Research Institute, UCLA School of Medicine, , CA 90095-1555;Department of Mathematics UCLA, , Los Angeles, CA 90095-1555;Laboratory of Neuro Imaging and Brain Research Institute, UCLA School of Medicine, , CA 90095-1555

  • Venue:
  • MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
  • Year:
  • 2008

Quantified Score

Hi-index 0.01

Visualization

Abstract

In this work, we find meaningfulparameterizations of cortical surfaces utilizing prior anatomical information in the form of anatomical landmarks (sulci curves) on the surfaces. Specifically we generate close to conformal parametrizations that also give a shape-basedcorrespondence between the landmark curves. We propose a variational energy that measures the harmonic energy of the parameterization maps, and the shape dissimilarity between mapped points on the landmark curves. The novelty is that the computed maps are guaranteed to give a shape-baseddiffeomorphism between the landmark curves. We achieve this by intrinsically modelling our search space of maps as flows of smooth vector fields that do not flow across the landmark curves, and by using the local surface geometry on the curves to define a shape measure. Such parameterizations ensure consistent correspondence between anatomical features, ensuring correct averaging and comparison of data across subjects. The utility of our model is demonstrated in experiments on cortical surfaces with landmarks delineated, which show that our computed maps give a shape-based alignment of the sulcal curves without significantly impairing conformality.