Supervised pattern recognition techniques in quantitative diagnostic ultrasound

Michael F. Insana, Robert F. Wagner, Brian S. Garra, Reza Momenan, Thomas H. Shawker

Research output: Contribution to journalArticlepeer-review


The methods of statistical pattern recognition are well suited to the problems of in vivo ultrasonic tissue characterization. This paper describes supervised pattern recognition methods for selecting features for tissue classification, calculating decision boundaries within the selected feature space, and evaluating the performance. This paper expands on the authors' previous work (Insana, 1986a) by addressing the considerations of dimensionality and sample size. These are important in classification problems where the underlying probability distributions are not completely known. Examples are given for the detection of diffuse liver disease in the clinical environment.

Original languageEnglish (US)
Pages (from-to)447-454
Number of pages8
JournalJournal of Clinical Engineering
Issue number6
StatePublished - 1988
Externally publishedYes


  • Acoustic properties
  • Hotelling trace
  • Random phasor sum
  • Receiver operating characteristic (ROC) analysis
  • Sample size
  • Scattering
  • Statistical pattern recognition
  • Statistical properties
  • Tissue characterization
  • Ultrasound

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biomedical Engineering


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