An Improved Adaptive Level Set Method for Image Segmentation

Li Zhang*, Kai Teng Wu, Ping Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

In order to improve the accuracy of image segmentation, an improved adaptive level set method is proposed based on level set evolution without re-initialization method and adaptive distance preserving level set evolution method. A new definition of weight coefficient in evolution equations is the main innovation of this paper. The improved method can detect certain object boundaries, interior and exterior contours of an object, edges of multi-objects and weak boundaries of an object by synthetic and real images numerical experiments. Numerical results show that the improved adaptive level set method has faster segmentation speed and higher segmentation accuracy compared with the previous two methods, especially in weak boundaries and edges of multi-objects segmentation problems.

Original languageEnglish
Article number1854013
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume32
Issue number5
DOIs
Publication statusPublished - 1 May 2018

Keywords

  • Level set method
  • geometric active contour model
  • image segmentation
  • partial differential equation
  • re-initialization

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