Active contours driven by local image fitting energy

Kaihua Zhang, Huihui Song, Lei Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

605 Citations (Scopus)

Abstract

A new region-based active contour model that embeds the image local information is proposed in this paper. By introducing the local image fitting (LIF) energy to extract the local image information, our model is able to segment images with intensity inhomogeneities. Moreover, a novel method based on Gaussian filtering for variational level set is proposed to regularize the level set function. It can not only ensure the smoothness of the level set function, but also eliminate the requirement of re-initialization, which is very computationally expensive. Experiments show that the proposed method achieves similar results to the LBF (local binary fitting) energy model but it is much more computationally efficient. In addition, our approach maintains the sub-pixel accuracy and boundary regularization properties.
Original languageEnglish
Pages (from-to)1199-1206
Number of pages8
JournalPattern Recognition
Volume43
Issue number4
DOIs
Publication statusPublished - 1 Apr 2010

Keywords

  • Active contour models
  • Chan-Vese (C-V) model
  • Image segmentation
  • LBF model

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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