TWO-PHASE SEGMENTATION FOR INTENSITY INHOMOGENEOUS IMAGES BY THE ALLEN-CAHN LOCAL BINARY FITTING MODEL

Chaoyu Liu, Zhonghua Qiao, Qian Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

This paper proposes a new variational model by integrating the Allen-Cahn term with a local binary fitting energy term for segmenting images with intensity inhomogeneity and noise. An inhomogeneous graph Laplacian initialization method (IGLIM) is developed to give the initial contour for two-phase image segmentation problems. To solve the Allen-Cahn equation derived from the variational model, we adopt the exponential time differencing (ETD) method for temporal discretization, and the central finite difference method for spatial discretization. The energy stability of proposed numerical schemes can be proved. Experiments on various images demonstrate the necessity and superiority of proper initialization and verify the capability of our model for two-phase segmentation of images with intensity inhomogeneity and noise.

Original languageEnglish
Pages (from-to)B177-B196
Number of pages20
JournalSIAM Journal on Scientific Computing
Volume44
Issue number1
DOIs
Publication statusPublished - Feb 2022

Keywords

  • Allen-Cahn equation
  • edge detection
  • energy stability
  • exponential time differencing method
  • image segmentation
  • inhomogeneous graph Laplacian

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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