Wavelet scalar-energy features for recognition of mammographic mass shapes

Lori Mann Bruce; Maria Kallergi; Andres Mendoza

doi:10.1117/12.342923

22 March 1999 Wavelet scalar-energy features for recognition of mammographic mass shapes

Lori Mann Bruce, Maria Kallergi, Andres Mendoza

Proceedings Volume 3723, Wavelet Applications VI; (1999) https://doi.org/10.1117/12.342923
Event: AeroSense '99, 1999, Orlando, FL, United States

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.

Citation Download Citation

Lori Mann Bruce, Maria Kallergi, and Andres Mendoza "Wavelet scalar-energy features for recognition of mammographic mass shapes", Proc. SPIE 3723, Wavelet Applications VI, (22 March 1999); https://doi.org/10.1117/12.342923

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