An Active Contour Model with Local Variance Force Term and Its Efficient Minimization Solver for Multiphase Image Segmentation

Chaoyu Liu, Zhonghua Qiao, Qian Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a general active contour model with a local variance force (LVF) term that can be applied to multiphase image segmentation problems. With the LVF, the proposed model is very effective in the segmentation of images with noise. A well-targeted minimization algorithm called ICTM-LVF is then designed to solve this model efficiently. This minimization algorithm, developed from the iterative convolution-thresholding method (ICTM), enjoys the energy-decaying property under some conditions and has highly efficient performance in the segmentation. To overcome the initialization issue of active contour models, we generalize the inhomogeneous graph Laplacian initialization method to the multiphase case and then apply it to give the initial contour of the ICTM-LVF solver. Numerical experiments are conducted on synthetic images and real images to demonstrate the capability of our initialization method, and the effectiveness of the LVF for noise robustness in the multiphase image segmentation.

Original languageEnglish
Pages (from-to)144-168
Number of pages25
JournalSIAM Journal on Imaging Sciences
Volume16
Issue number1
DOIs
Publication statusPublished - Mar 2023

Keywords

  • active contour model
  • image segmentation
  • inhomogeneous graph Laplacian
  • iterative convolution-thresholding method
  • local variance force

ASJC Scopus subject areas

  • General Mathematics
  • Applied Mathematics

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