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

Preserving 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 th...

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Bibliographic Details
Published in:arXiv.org 2021-02
Main Authors: Pan, Huizhu, Song, Jintao, Liu, Wanquan, Li, Ling, Zhou, Guanglu, Tan, Lu, Chen, Shichu
Format: Article
Language:English
Subjects:
Algorithms
Contours
Image segmentation
Iterative methods
Shape
Topology
Online Access:Get full text
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Summary:Preserving 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. In our paper, we propose an alternative solution to the SR using the Split Bregman method. Our algorithm breaks the problem down into simpler sub-problems to use lower-order evolution equations and a simple projection scheme rather than re-initialization. The sub-problems can be solved via fast Fourier transform (FFT) or an approximate soft thresholding formula which maintains stability, shortening the convergence time, and reduces the memory requirement. The Split Bregman and AOS algorithms are compared theoretically and experimentally.
ISSN:2331-8422