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: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes a procedure 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: How well does the clustering method group the data? and 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
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsLeonard A. Ferrari
Place of PublicationBellingham, WA, USA
PublisherSPIE
Pages155-161
Number of pages7
Volume768
ISBN (Print)0892528036
StatePublished - 1987
Externally publishedYes

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

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