Multi-phase image segmentation by the Allen–Cahn Chan–Vese model

Chaoyu Liu, Zhonghua Qiao, Qian Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

This paper proposes an Allen–Cahn Chan–Vese model to settle the multi-phase image segmentation. We first integrate the Allen–Cahn term and the Chan–Vese fitting energy term to establish an energy functional, whose minimum locates the segmentation contour. The subsequent minimization process can be attributed to variational calculation on fitting intensities and the solution approximation of several Allen–Cahn equations, wherein n Allen–Cahn equations are enough to partition m=2n segments. The derived Allen–Cahn equations are solved by efficient numerical solvers with exponential time integrations and finite difference space discretization. The discrete maximum bound principle and energy stability of the proposed numerical schemes are proved. Finally, the capability of our segmentation method is verified in various experiments for different types of images.

Original languageEnglish
Pages (from-to)207-220
Number of pages14
JournalComputers and Mathematics with Applications
Volume141
DOIs
Publication statusPublished - 1 Jul 2023

Keywords

  • Allen–Cahn Chan–Vese model
  • Energy stability
  • Graph Laplacian
  • Maximum principle
  • Multi-phase image segmentation

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computational Theory and Mathematics
  • Computational Mathematics

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