Application of cluster analysis and unsupervised learning to multivariate tissue characterization

Reza Momenan, Michael F. Insana, Robert F. Wagner, Brian S. Garra, Murray H. Loew

Research output: Contribution to journalArticlepeer-review


This paper describes procedures for classifying tissue types from unlabeled acoustic measurements (data type unknown) using unsupervised cluster analysis. These techniques are being applied to unsupervised ultrasonic image segmentation and tissue characterization. The performance of a new clustering technique is measured and compared with supervised methods, such as a linear Bayes classifier. In these comparisons, two objectives are sought: a) How well does the clustering method group the data? b) Do the clusters correspond to known tissue classes? The first question is investigated by a measure of cluster similarity and dispersion. The second question involves a comparison with a supervised technique using labeled data.

Original languageEnglish (US)
Pages (from-to)455-461
Number of pages7
JournalJournal of Clinical Engineering
Issue number6
StatePublished - 1988
Externally publishedYes


  • Cluster similarity measure, tissue analysis
  • Clusters, tissue characterization
  • Hotelling trace
  • K-Means
  • Tissue characterization, ultrasound
  • Ultrasound, tissue analysis
  • Unsupervised classifier, tissue types

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biomedical Engineering


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