A comparison of fuzzy clustering approaches for quantification of microarray gene expression

Yu Ping Wang, Maheswar Gunampally, Jie Chen, Douglas Bittel, Merlin G. Butler, Wei Wen Cai

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Despite the widespread application of microarray imaging for biomedical imaging research, barriers still exist regarding its reliability for clinical use. A critical major problem lies in accurate spot segmentation and the quantification of gene expression level (mRNA) from the microarray images. A variety of commercial and research freeware packages are available, but most cannot handle array spots with complex shapes such as donuts and scratches. Clustering approaches such as k-means and mixture models were introduced to overcome this difficulty, which use the hard labeling of each pixel. In this paper, we apply fuzzy clustering approaches for spot segmentation, which provides soft labeling of the pixel. We compare several fuzzy clustering approaches for microarray analysis and provide a comprehensive study of these approaches for spot segmentation. We show that possiblistic c-means clustering (PCM) provides the best performance in terms of stability criterion when testing on both a variety of simulated and real microarray images. In addition, we compared three statistical criteria in measuring gene expression levels and show that a new asymptotically unbiased statistic is able to quantify the gene expression level more accurately.

Original languageEnglish (US)
Pages (from-to)305-320
Number of pages16
JournalJournal of Signal Processing Systems
Issue number3
StatePublished - Mar 2008
Externally publishedYes


  • Fuzzy clustering
  • Image segmentation
  • Microarray
  • Microarray gridding
  • Segmentation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modeling and Simulation
  • Hardware and Architecture


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