Entropy tolerant fuzzy c-means in medical images

S. R. Kannan, S. Ramathilagam, R. Devi, Yueh Min Huang

Research output: Contribution to journalArticlepeer-review


Segmenting the Dynamic Contrast-Enhanced Breast Magnetic Resonance Images (DCE-BMRI) is an extremely important task to diagnose the disease because it has the highest specificity when acquired with high temporal and spatial resolution and is also corrupted by heavy noise, outliers, and other imaging artifacts. In this paper, we intend to develop efficient robust segmentation algorithms based on fuzzy clustering approach for segmenting the DCE-BMRs. Our proposed segmentation algorithms have been amalgamated with effective kernel-induced distance measure on standard fuzzy c-means algorithm along with the spatial neighborhood information, entropy term, and tolerance vector into a fuzzy clustering structure for segmenting the DCE-BMRI. The significant feature of our proposed algorithms is its capability to find the optimal membership grades and obtain effective cluster centers automatically by minimizing the proposed robust objective functions. Also, this article demonstrates the superiority of the proposed algorithms for segmenting DCE-BMRI in comparison with other recent kernel-based fuzzy c-means techniques. Finally the clustering accuracies of the proposed algorithms are validated by using silhouette method in comparison with existed fuzzy clustering algorithms.

Original languageEnglish
Pages (from-to)447-462
Number of pages16
JournalJournal of Innovative Optical Health Sciences
Issue number4
Publication statusPublished - 2011 Oct

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Medicine (miscellaneous)
  • Atomic and Molecular Physics, and Optics
  • Biomedical Engineering


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