IEEE Trans Med Imaging. 2015 Feb;34(2):662-71. doi: 10.1109/TMI.2014.2365436. Epub 2014 Oct 28.
We hypothesize that quantification of structural similarity or dissimilarity between paired mammographic regions can be effective in detecting asymmetric signs of breast cancer. Bilateral masking procedures are applied for this purpose by using automatically detected anatomical landmarks. Changes in structural information of the extracted regions are investigated using spherical semivariogram descriptors and correlation-based structural similarity indices in the spatial and complex wavelet domains. The spatial distribution of grayscale values as well as of the magnitude and phase responses of multidirectional Gabor filters are used to represent the structure of mammographic density and of the directional components of breast tissue patterns, respectively. A total of 188 mammograms from the DDSM and mini-MIAS databases, consisting of 47 asymmetric cases and 47 normal cases, were analyzed. For the combined dataset of mammograms, areas under the receiver operating characteristic curves of 0.83, 0.77, and 0.87 were obtained, respectively, with linear discriminant analysis, the Bayesian classifier, and an artificial neural network with radial basis functions, using the features selected by stepwise logistic regression and leave-one-patient-out cross-validation. Two-view analysis provided accuracy up to 0.94, with sensitivity and specificity of 1 and 0.88, respectively.
我们假设，对配对乳房区域之间的结构相似性或差异性进行定量分析，可以有效地检测乳腺癌的不对称征象。为此，我们采用自动检测的解剖学标志，通过双侧掩蔽程序来实现这一目标。使用基于球的半变异描述符和基于相关性的结构相似性指数，研究了提取区域的结构信息在空间和复小波域中的变化。灰度值的空间分布，以及多方向 Gabor 滤波器的幅度和相位响应，分别用于表示乳房密度的结构和乳房组织模式的方向分量的结构。总共分析了来自 DDSM 和 mini-MIAS 数据库的 188 张乳房 X 光片，其中包括 47 个不对称病例和 47 个正常病例。对于包含所有乳房 X 光片的综合数据集，通过逐步逻辑回归和留一患者交叉验证，使用特征选择，线性判别分析、贝叶斯分类器和具有径向基函数的人工神经网络，分别获得了 0.83、0.77 和 0.87 的接收器操作特征曲线下面积。双视图分析的准确率高达 0.94，灵敏度和特异性分别为 1 和 0.88。
