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

Wen Feng Kuo, Chi Yuan Lin, Wei Yen Hsu

研究成果: 雜誌貢獻文章同行評審

8 引文 斯高帕斯(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.
頁(從 - 到)5255-5267
期刊International Journal of Innovative Computing, Information and Control
出版狀態已發佈 - 九月 2011

ASJC Scopus subject areas

  • 計算機理論與數學
  • 資訊系統
  • 軟體
  • 理論電腦科學


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