Unsupervised multiphase segmentation: A phase balancing model

Berta Sandberg*, Sung Ha Kang, Tony Chan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Variational models have been studied for image segmentation application since the Mumford-Shah functional was introduced in the late 1980s. In this paper, we focus on multiphase segmentation with a new regularization term that yields an unsupervised segmentation model. We propose a functional that automatically chooses a favorable number of phases as it segments the image. The primary driving force of the segmentation is the intensity fitting term while a phase scale measure complements the regularization term.We propose a fast, yet simple, brute-force numerical algorithm and present experimental results showing the robustness and stability of the proposed model.

Original languageEnglish (US)
Article number5238600
Pages (from-to)119-130
Number of pages12
JournalIEEE Transactions on Image Processing
Volume19
Issue number1
DOIs
StatePublished - Jan 1 2010

Keywords

  • Cheeger Set
  • Image segmentation
  • Multiphase
  • Scale
  • Variational model

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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