Reinitialization-free level set evolution via reaction diffusion

Kaihua Zhang, Lei Zhang, Huihui Song, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

176 Citations (Scopus)

Abstract

This paper presents a novel reaction-diffusion (RD) method for implicit active contours that is completely free of the costly reinitialization procedure in level set evolution (LSE). A diffusion term is introduced into LSE, resulting in an RD-LSE equation, from which a piecewise constant solution can be derived. In order to obtain a stable numerical solution from the RD-based LSE, we propose a two-step splitting method to iteratively solve the RD-LSE equation, where we first iterate the LSE equation, then solve the diffusion equation. The second step regularizes the level set function obtained in the first step to ensure stability, and thus the complex and costly reinitialization procedure is completely eliminated from LSE. By successfully applying diffusion to LSE, the RD-LSE model is stable by means of the simple finite difference method, which is very easy to implement. The proposed RD method can be generalized to solve the LSE for both variational level set method and partial differential equation-based level set method. The RD-LSE method shows very good performance on boundary antileakage. The extensive and promising experimental results on synthetic and real images validate the effectiveness of the proposed RD-LSE approach.
Original languageEnglish
Article number6272358
Pages (from-to)258-271
Number of pages14
JournalIEEE Transactions on Image Processing
Volume22
Issue number1
DOIs
Publication statusPublished - 2 Jan 2013

Keywords

  • Active contours
  • image segmentation
  • level set
  • partial differential equation (PDE)
  • reaction-diffusion
  • variational method

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Cite this