An accurate and practical explicit hybrid method for the chan-vese image segmentation model

Darae Jeong, Sangkwon Kim, Chaeyoung Lee, Junseok Kim

Research output: Contribution to journalArticle

Abstract

In this paper, we propose a computationally fast and accurate explicit hybrid method for image segmentation. By using a gradient flow, the governing equation is derived from a phase-field model to minimize the Chan-Vese functional for image segmentation. The resulting governing equation is the Allen-Cahn equation with a nonlinear fidelity term. We numerically solve the equation by employing an operator splitting method. We use two closed-form solutions and one explicit Euler's method, which has a mild time step constraint. However, the proposed scheme has the merits of simplicity and versatility for arbitrary computational domains. We present computational experiments demonstrating the efficiency of the proposed method on real and synthetic images.

Original languageEnglish
Article number1173
JournalMathematics
Volume8
Issue number7
DOIs
Publication statusPublished - 2020 Jul

Keywords

  • Allen-Cahn equation
  • Finite difference method
  • Image processing

ASJC Scopus subject areas

  • Mathematics(all)

Fingerprint Dive into the research topics of 'An accurate and practical explicit hybrid method for the chan-vese image segmentation model'. Together they form a unique fingerprint.

  • Cite this