2019/05/27 by Jintao Song, Huizhu Pan, Song, Jintao +7
Computer Science · Mathematics · #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Mathematical Biology Tumor Growth #Medical Image Segmentation Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1905.11192
17pages,9figure
openalex publication_date 2019/05/27 · openalex created_date 2019/06/07 · arxiv created 2019/08/07 · arxiv updated 2019/08/08 · openalex updated_date 2026/07/28
In order to completely separate objects with large sections of occluded boundaries in an image, we devise a new variational level set model for image segmentation combining the Chan-Vese model with elastica and landmark constraints. For computational efficiency, we design its Augmented Lagrangian Method (ALM) or Alternating Direction Method of Multiplier (ADMM) method by introducing some auxiliary variables, Lagrange multipliers, and penalty parameters. In each loop of alternating iterative optimization, the sub-problems of minimization can be easily solved via the Gauss-Seidel iterative method and generalized soft thresholding formulas with projection, respectively. Numerical experiments show that the proposed model can not only recover larger broken boundaries but can also improve segmentation efficiency, as well as decrease the dependence of segmentation on parameter tuning and initialization.