Selective level set segmentation using fuzzy region competition

Bing Nan Li, Jing Qin, Rong Wang, Meng Wang, Xuelong Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

36 Citations (Scopus)


Deformable models and level set methods have been extensively investigated for computerized image segmentation. However, medical image segmentation is yet one of open challenges owing to diversified physiology, pathology, and imaging modalities. Existing level set methods suffer from some inherent drawbacks in face of noise, ambiguity, and inhomogeneity. It is also refractory to control level set segmentation that is dependent on image content and evolutional strategies. In this paper, a new level set formulation is proposed by using fuzzy region competition for selective image segmentation. It is able to detect and track the arbitrary combination of selected objects or image components. To the best of our knowledge, this new formulation should be one of the first proposals in a framework of region competition for selective segmentation. Experiments on both synthetic and real images validate its advantages in selective level set segmentation.
Original languageEnglish
Article number7536187
Pages (from-to)4777-4788
Number of pages12
JournalIEEE Access
Publication statusPublished - 1 Jan 2016


  • Fuzzy control
  • image segmentation
  • level set methods
  • region competition
  • selective segmentation

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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