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Geodesic Distances to Landmarks for Dense Correspondence on Ensembles of Complex Shapes
Manasi Datar1, Ilwoo Lyu2, SunHyung Kim3, Joshua Cates1, 4, Martin A. Styner2, 3, and Ross Whitaker1
1Scientific Computing and Imaging Institute, University of Utah, USA
2Department of Computer Science, University of North Carolina at Chapel Hill, USA
3Department of Psychiatry, University of North Carolina at Chapel Hill, USA
4CARMA Center, University of Utah, USA
Abstract. Establishing correspondence points across a set of biomedical shapes is an important technology for a variety of applications that rely on statistical analysis of individual subjects and populations. The inherent complexity (e.g. cortical surface shapes) and variability (e.g. cardiac chambers) evident in many biomedical shapes introduce significant challenges in finding a useful set of dense correspondences. Application specific strategies, such as registration of simplified (e.g. inflated or smoothed) surfaces or relying on manually placed landmarks, provide some improvement but suffer from limitations including increased computational complexity and ambiguity in landmark placement. This paper proposes a method for dense point correspondence on shape ensembles using geodesic distances to a priori landmarks as features. A novel set of numerical techniques for fast computation of geodesic distances to point sets is used to extract these features. The proposed method minimizes the ensemble entropy based on these features, resulting in isometry invariant correspondences in a very general, flexible framework.
LNCS 8150, p. 19 ff.
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© Springer-Verlag Berlin Heidelberg 2013