Application of pattern recognition techniques in ultrasound tissue characterization

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An approach for the application of multivariate pattern recognition techniques to detection of diffuse and focal disease using acoustic data is reviewed. Supervised and unsupervised techniques are implemented to design the best ultrasonic tissue signature for a given task from a set of measurements. The performances of both techniques are evaluated and compared using several methods. However, it is desirable to utilize a technique that quantitatively detects and displays the heterogeneity of an ultrasound image. It is shown that, for a particular task, choosing features with physical significance (i.e., related to the variations in structure and environment due to changes caused by pathology of the tissue) will make the classification of the data more robust. It is also shown that the success of combining supervised and unsupervised techniques using such features extends beyond discrimination of one class of data from the other and that the approach can be used to grade and the variations in the same tissue type.

Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherPubl by IEEE
Pages608-612
Number of pages5
ISBN (Print)0818620625
StatePublished - 1990
Externally publishedYes
EventProceedings of the 10th International Conference on Pattern Recognition - Atlantic City, NJ, USA
Duration: Jun 16 1990Jun 21 1990

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1

Other

OtherProceedings of the 10th International Conference on Pattern Recognition
CityAtlantic City, NJ, USA
Period6/16/906/21/90

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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