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Mammography is routinely used to screen for breast cancer. However, the radiological interpretation of mammogram images is complicated by the heterogeneous nature of normal breast tissue and the fact that cancers are often of the same radiographic density as normal tissue. In this work, we use wavelets to quantify spectral slopes of breast cancer cases and controls and demonstrate their value in classifying images. In addition, we propose asymmetry statistics to be used in forming features, which improve the classification result. For the best classification procedure, we achieve approximately 77% accuracy (sensitivity=73%, specificity=84%) in classifying mammograms with and without cancer. Copyright © 2017 John WileySons, Ltd. |
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