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Proceedings Paper

Wavelet scalar-energy features for recognition of mammographic mass shapes
Author(s): Lori Mann Bruce; Maria Kallergi; Andres Mendoza
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Paper Abstract

The goal of this study is to analyze the use of wavelet- based shape features for automated recognition of mammographic mass shapes. Two sets of shape features are used. The first set includes wavelet-based scalar-energy features. The mass boundary radial distance memory is decomposed using a discrete wavelet transform. The energy of the coefficients at each scale are computed, and these energy values are then used to form a feature vector. Several mother wavelets are used for the wavelet-based shape features: Daubechies-3 (DB3), DB5, DB7, DB9, DB11, DB13, DB15, Coiflets-3 (C3), C5, Symlets-2 (S2), S4, S6, and S8. The second set includes the following traditional features: radial distance mean, standard deviation, zero-crossing count, roughness index, entropy, and compactness. For each set of shape features, linear discriminant analysis is used to appropriately weight the features, and a minimum Euclidean distance classifier is used to separate the shapes into three classes: round, lobular, and irregular. The classification results, as well as false positive and false negative rates, are compared for each set of shape features.

Paper Details

Date Published: 22 March 1999
PDF: 7 pages
Proc. SPIE 3723, Wavelet Applications VI, (22 March 1999); doi: 10.1117/12.342923
Show Author Affiliations
Lori Mann Bruce, Univ. of Nevada/Las Vegas (United States)
Maria Kallergi, Univ. of South Florida (United States)
Andres Mendoza, Univ. of Nevada/Las Vegas (United States)


Published in SPIE Proceedings Vol. 3723:
Wavelet Applications VI
Harold H. Szu, Editor(s)

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