A Level Set Approach to Image Segmentation with Intensity Inhomogeneity

Kaihua Zhang, Lei Zhang, Kin Man Lam, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

250 Citations (Scopus)

Abstract

It is often a difficult task to accurately segment images with intensity inhomogeneity, because most of representative algorithms are region-based that depend on intensity homogeneity of the interested object. In this paper, we present a novel level set method for image segmentation in the presence of intensity inhomogeneity. The inhomogeneous objects are modeled as Gaussian distributions of different means and variances in which a sliding window is used to map the original image into another domain, where the intensity distribution of each object is still Gaussian but better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field with the original signal within the window. A maximum likelihood energy functional is then defined on the whole image region, which combines the bias field, the level set function, and the piecewise constant function approximating the true image signal. The proposed level set method can be directly applied to simultaneous segmentation and bias correction for 3 and 7T magnetic resonance images. Extensive evaluation on synthetic and real-images demonstrate the superiority of the proposed method over other representative algorithms.
Original languageEnglish
Article number7059203
Pages (from-to)546-557
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume46
Issue number2
DOIs
Publication statusPublished - 1 Feb 2016

Keywords

  • Active contour model
  • bias field correction
  • intensity inhomogeneity
  • level set method
  • segmentation

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this