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Cell segmentation based on spatial information improved intuitionistic fcm combined with FOPSO

  • Chuxiong Sun
  • , Xiangzhi Bai*
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Fuzzy c-means clustering (FCM) algorithm has been proved to be effective for image segmentation. However, it is sensitive to the noises and initialization. FCM could not effectively segment cell images with inhomogeneity and complicate adhesives. Aimed to overcome these disadvantages, this paper proposes a cell image segmentation algorithm using spatial information improved intuitionistic fuzzy c-means clustering (SI-IFCM) combined with fractional-order velocity based particle swarm optimization (FOPSO). SI-IFCM and FOPSO will iterate alternately with different object functions to obtain the clustering result. Experimental results demonstrate the advantages of our algorithm for cell segmentation comparing with state-of-arts algorithms.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages4457-4461
Number of pages5
ISBN (Electronic)9781509021758
DOIs
StatePublished - 2 Jul 2017
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 17 Sep 201720 Sep 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Conference

Conference24th IEEE International Conference on Image Processing, ICIP 2017
Country/TerritoryChina
CityBeijing
Period17/09/1720/09/17

Keywords

  • Cell image segmentation
  • Fuzzy clustering
  • Particle swarm optimization
  • Spatial information

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