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In order to completely separate objects with large sections of occludedboundaries in an image, we devise a new variational level set model for imagesegmentation combining the Chan-Vese model with elastica and landmarkconstraints. For computational efficiency, we design its Augmented LagrangianMethod (ALM) or Alternating Direction Method of Multiplier (ADMM) method byintroducing some auxiliary variables, Lagrange multipliers, and penaltyparameters. In each loop of alternating iterative optimization, thesub-problems of minimization can be easily solved via the Gauss-Seideliterative method and generalized soft thresholding formulas with projection,respectively. Numerical experiments show that the proposed model can not onlyrecover larger broken boundaries but can also improve segmentation efficiency,as well as decrease the dependence of segmentation on parameter tuning andinitialization.

The Chan-Vese Model with Elastica and Landmark Constraints for Image Segmentation