Fuzzy based seeded region growing for image segmentation

Chung Chia Kang, Wen June Wang

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

11 Scopus citations

Abstract

This study proposes a novel seeded region growing based image segmentation method for both color and gray level images. The proposed fuzzy edge detection method, that only detects the connected edge, is used with fuzzy image pixel similarity to automatically select the initial seeds. The fuzzy distance is used to determine the difference between the pixel and region in the consequent regions growing, in which the conventional regions growing is modified to ensure that the pixel on the edge is processed later than other pixels, and the difference between two regions in the regions merging. In the simulations, the proposed method outperforms other existing segmentation methods.

Original languageEnglish
Title of host publicationNAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society
DOIs
StatePublished - 2009
Event2009 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2009 - Cincinnati, OH, United States
Duration: 14 Jun 200917 Jun 2009

Publication series

NameAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS

Conference

Conference2009 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2009
Country/TerritoryUnited States
CityCincinnati, OH
Period14/06/0917/06/09

Keywords

  • Color image
  • Edge detection
  • Fuzzy logic
  • Gray level image
  • Image segmentation
  • Seeded region growing

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