Using the Split Bregman Algorithm to Solve the Self-repelling Snake Model

Abstract

Preserving the contour topology during image segmentation is useful in many practical scenarios. By keeping the contours isomorphic, it is possible to prevent over-segmentation and under-segmentation, as well as to adhere to given topologies. The self-repelling snake model (SR) is a variational model that preserves contour topology by combining a non-local repulsion term with the geodesic active contour model (GAC). The SR is traditionally solved using the additive operator splitting (AOS) scheme. Although this solution is stable, the memory requirement grows quickly as the image size increases. In our paper, we propose an alternative solution to the SR using the Split Bregman method.Our algorithm breaks the problem down into simpler subproblems to use lowerorder evolution equations and approximation schemes. The memory usage is significantly reduced as a result. Experiments show comparable performance to the original algorithm with shorter iteration times.