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Proceedings Paper

Wavelets for computer-aided breast cancer diagnosis
Author(s): Lemuel R. Myers; Catherine M. Kocur; Steven K. Rogers; Chris Eisenbies; Jeffrey W. Hoffmeister
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Paper Abstract

More than 50 million women over the age of 40 are currently at risk for breast cancer in the United States. Computer-aided diagnosis, used as a `second opinion' to radiologists, will aid in decreasing the number of false readings of mammograms. A novel feature extraction method is presented that provides increased classification power. Wavelets, previously only exploited for their segmentation benefits, are explored as features for classification. Daubechies4, Daubechies20, and biorthogonal wavelets are each investigated. Applied to 94 difficult-to- diagnose digitized microcalcification cases, performance is 74 percent correct classifications. Feature selection techniques are presented which further improve wavelet classification performance to 88 percent correct classification.

Paper Details

Date Published: 6 April 1995
PDF: 10 pages
Proc. SPIE 2491, Wavelet Applications II, (6 April 1995); doi: 10.1117/12.205438
Show Author Affiliations
Lemuel R. Myers, Air Force Institute of Technology (United States)
Catherine M. Kocur, Air Force Institute of Technology (United States)
Steven K. Rogers, Air Force Institute of Technology (United States)
Chris Eisenbies, Air Force Institute of Technology (United States)
Jeffrey W. Hoffmeister, Air Force Armstrong Labs. (United States)


Published in SPIE Proceedings Vol. 2491:
Wavelet Applications II
Harold H. Szu, Editor(s)

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