A level-set method based on global and local regions for image segmentation

Yu Qian Zhao, Xiao Fang Wang, Frank Y. Shih, Gang Yu

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

This paper presents a new level-set method based on global and local regions for image segmentation. First, the image fitting term of Chan and Vese (CV) model is adapted to detect the image's local information by convolving a Gaussian kernel function. Then, a global term is proposed to detect large gradient amplitude at the outer region. The new energy function consists of both local and global terms, and is minimized by the gradient descent method. Experimental results on both synthetic and real images show that the proposed method can detect objects in inhomogeneous, low-contrast, and noisy images more accurately than the CV model, the local binary fitting model, and the Lankton and Tannenbaum model.

Original languageEnglish (US)
Article number1255004
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume26
Issue number1
DOIs
StatePublished - Feb 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Chan and Vese model
  • Image segmentation
  • active contour model
  • local binary fitting model

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