Pattern recognition methods for optimizing multivariate tissue signatures in diagnostic ultrasound

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

Research output: Contribution to journalArticlepeer-review

Abstract

Described is a supervised parametric approach to the detection and classification of disease from acoustic data. Statistical pattern recognition techniques are implemented to design the best ultrasonic tissue signature from a set of measurements and for a given task, and to rate its performance in a way that can be compared with other diagnostic tools. In this paper, we considered combinations of four ultrasonic tissue parameters to discriminate, in vivo, between normal liver and chronic active hepatitis. The separation between normal and diseased samples was made by application of the Bayes decision rule for minimum risk which includes the prior probability for the presence of disease and the cost of misclassification. Large differences in classification performance of various tissue parameter combinations were demonstrated using the Hotelling trace criterion (HTC) and receiver operating characteristic (ROC) analysis. The ability of additional measurements to increase or decrease discrimin-ability, even measurements from other diagnostic modalities, can be evaluated directly in this manner.

Original languageEnglish (US)
Pages (from-to)165-180
Number of pages16
JournalUltrasonic Imaging
Volume8
Issue number3
DOIs
StatePublished - Jul 1986
Externally publishedYes

Keywords

  • Classification
  • Hotelling trace criterion
  • ROC analysis
  • discriminant analysis
  • hepatic disease
  • pattern recognition
  • principal components
  • quantitative ultrasound

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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