Convexity Shape Prior for Level Set-Based Image Segmentation Method

Shi Yan*, Xue-Cheng TAI, Jun Liu, Hai Yang Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

In this paper, we propose an image segmentation model that incorporates convexity shape priori using level set representations. In the past decade, several discrete and continuous methods have been developed to solve this problem. Our method comes from the observation that the signed distance function of a convex region must be a convex function. Based on this observation, we transfer the complicated geometrical convexity shape priori into some simple constraints on the signed distance function. We propose a simple algorithm to keep these constraints exactly. The proposed method could be easily applied to level set based segmentation models, such as the well-known Chan-Vese mode and the active contour models. By setting some good initial curves, the proposed method can easily segment convex objects from images with complicated background. We demonstrate the performance of the proposed methods on both synthetic images and real images, as well as the comparison to some state-of-the-art methods.

Original languageEnglish
Article number9109685
Pages (from-to)7141-7152
Number of pages12
JournalIEEE Transactions on Image Processing
Volume29
DOIs
Publication statusPublished - 2020

Scopus Subject Areas

  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Chan-Vese model
  • Convexity shape prior
  • image segmentation
  • level set method

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