Circulant dissimilarity-based shape registration for object segmentation

Abstract

A shape prior based object segmentation is developed in this paper by using a shape transformation distance to constrain object contour evolution. In the proposed algorithm, the transformation distance measures the dissimilarity between two unaligned shapes by cyclic shift, which is called "circulant dissimilarity". This dissimilarity with respect to translation and rotation of the object shape is represented by circular convolution, which could be efficiently computed by using fast Fourier transform. Given a set of training shapes, the kernel density estimate is adopted to model shape prior. By integrating low-level image feature, high-level shape prior and transformation distance, a variational segmentation model is proposed to solve the transformation invariance of shape prior. Numerical experiments demonstrate that circulant dissimilarity based shape registration outperforms the iterative optimization on explicit pose parameters, and show promising results and highlight the potential of the method for object registration and segmentation.

A Convexity-Preserving Level-Set Method for the Segmentation of Tumor Organoids

“…However, the ACMs may fail to segment some complex images, for instance due to intensity inhomogeneity. Furthermore, higher level information can be incorporated into the ACMs, such as shape priors [ 17 , 18 ], Euler elasticity energy [ 19 , 20 ], and convexity preservation [ 21 , 22 ].…”