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Proceedings Paper

Classification of masses in digitized mammograms with features in the wavelet transform domain
Author(s): John B. Weaver; Dennis M. Healy Jr.; Helene Nagy M.D.; Steven P. Poplack; Jian Lu; Tracy Sauerland; David Langdon
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Paper Abstract

One of the primary visual properties used by radiologists in classifying masses is the sharpness of the edge of the mass. Wavelet transforms can be thought of as multiscale edge detectors. We report using the edge detection and classification properties of wavelet transforms to help classify masses on mammograms. We digitized six masses from mammograms: three benign and three malignant. Our preliminary results indicate that edge properties of masses in mammograms can be obtained from features in the wavelet transform domain. These edge properties can be used to help classify masses prior to biopsy. In particular, the change in the direction of the edge gradient at intermediate scales is indicative of malignancy. This work must be extended to a much larger sample size. The larger sample size will allow other measures to be used. More importantly the interaction between measures can then be observed. Undoubtedly a combination of measures will be required to classify masses accurately.

Paper Details

Date Published: 15 March 1994
PDF: 7 pages
Proc. SPIE 2242, Wavelet Applications, (15 March 1994); doi: 10.1117/12.170069
Show Author Affiliations
John B. Weaver, Dartmouth-Hitchcock Medical Ctr. (United States)
Dennis M. Healy Jr., Dartmouth College (United States)
Helene Nagy M.D., Dartmouth-Hitchcock Medical Ctr. (United States)
Steven P. Poplack, Dartmouth-Hitchcock Medical Ctr. (United States)
Jian Lu, Dartmouth College (United States)
Tracy Sauerland, Dartmouth-Hitchcock Medical Ctr. (United States)
David Langdon, Dartmouth-Hitchcock Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 2242:
Wavelet Applications
Harold H. Szu, Editor(s)

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