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

doi:10.1117/12.940261

10 September 1987 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

Proceedings Volume 0768, Pattern Recognition and Acoustical Imaging; (1987) https://doi.org/10.1117/12.940261
Event: International Symposium on Pattern Recognition and Acoustical Imaging, 1987, Newport Beach, CA, United States

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.

Citation Download Citation

Reza Momenan, Michael F. Insana, Robert F. Wagner, Brian S. Garra, and Murray H. Loew "Application Of Cluster Analysis And Unsupervised Learning To Multivariate Tissue Characterization", Proc. SPIE 0768, Pattern Recognition and Acoustical Imaging, (10 September 1987); https://doi.org/10.1117/12.940261

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