Medical image segmentation using the combination of watershed and FCM clustering algorithms

Wen Feng Kuo, Chi Yuan Lin, Wei Yen Hsu

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


In this study, a new image segmentation technique that combines watershed algorithm and fuzzy clustering algorithms is proposed to minimize undesirable oversegmentation. Watershed algorithm invariably produces over-segmentation due to noise or local irregularities in the gradient images. In the proposed scheme, first, it presents a region merging method based on employing the Markov Random Field (MRF) model on the Region Adjacency Graph (RAG) to refine the quality of watershed algorithm, and then, the relationship of inter-region similarities is then performed by involving the spatial domain (watershed) and feature spaces (clustering) into image mapping in order to determine optimal region merging. To obtain the spatial domain and feature spaces representation of the image, spatial graph representation is used, which is derived from the watershed partitioning and feature spaces representation acquired from the Fuzzy C-Means (FCM) clustering technique. Experimental results show that the proposed technique gives more promising segmentation results in comparison with the conventional watershed algorithm by means of the assessment of several brain phantom and real data.

Original languageEnglish
Pages (from-to)5255-5267
Number of pages13
JournalInternational Journal of Innovative Computing, Information and Control
Issue number9
Publication statusPublished - Sept 2011
Externally publishedYes


  • Clustering
  • Fuzzy c-means
  • Image segmentation
  • Region adjacency graph
  • Watershed

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Information Systems
  • Software
  • Theoretical Computer Science


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