Roberts T, Newell M, Auffermann W, Vidakovic B
H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, 765 Ferst Drive NW, Atlanta, GA, 30332, U.S.A.
Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1365 Clifton Road NE, Suite AT-504, Atlanta, GA, 30322, U.S.A.
Stat Med. 2017 May 30;36(12):1989-2000. doi: 10.1002/sim.7264. Epub 2017 Feb 22.
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 Wiley & Sons, Ltd.
乳房X线摄影术通常用于筛查乳腺癌。然而，由于正常乳腺组织的异质性以及癌症通常与正常组织具有相同的放射密度这一事实，乳房X线摄影图像的放射学解读变得复杂。在这项工作中，我们使用小波来量化乳腺癌病例和对照的光谱斜率，并证明它们在图像分类中的价值。此外，我们提出了用于形成特征的不对称统计量，这提高了分类结果。对于最佳分类程序，我们在对有癌和无癌的乳房X线照片进行分类时，准确率约为77%（敏感性=73%，特异性=84%）。版权所有© 2017约翰·威利父子有限公司。
