Share Email Print

Proceedings Paper

Application Of Cluster Analysis And Unsupervised Learning To Multivariate Tissue Characterization
Author(s): Reza Momenan; Michael F. Insana; Robert F. Wagner; Brian S. Garra; Murray H. Loew
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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 characteriza-tion. 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.

Paper Details

Date Published: 10 September 1987
PDF: 7 pages
Proc. SPIE 0768, Pattern Recognition and Acoustical Imaging, (10 September 1987); doi: 10.1117/12.940261
Show Author Affiliations
Reza Momenan, The George Washington University (United States)
Michael F. Insana, FDA (United States)
Robert F. Wagner, FDA (United States)
Brian S. Garra, National Institutes of Health (United States)
Murray H. Loew, The George Washington University (United States)

Published in SPIE Proceedings Vol. 0768:
Pattern Recognition and Acoustical Imaging
Leonard A. Ferrari, Editor(s)

© SPIE. Terms of Use
Back to Top