Curve skeleton-based shape representation and classification

Abstract

Computing the average anatomy and measuring the anatomical variability within a group of subjects are common practices in Computational Anatomy. In this paper, we propose a statistical analysis framework for 2D/3D shapes. At the core of the framework is a parametric shape representation formulated as a concatenation of skeleton points and the discs centered at the points. This shape representation possesses an excellent capability of capturing both global structures and local details. The constructed Riemannian manifold shape space provides a mathematically sound foundation for various groupwise operations, such as calculating the mean shape and conducting structure-specific normalization. Experiments with 2D shapes and 3D human brain structures show the effectiveness of our framework in calculating the distances among different shapes.