This paper proposes Local Photometric Attributes (LPA) for the characterization of mammographic masses as benign or malignant. LPA measures the local information over the optical density image which suppresses the background region and provides more details about the mass lesion. The evaluation of the proposed approach is conducted by incorporating the mammograms of two benchmark databases—mini-MIAS and DDSM where a ten-fold cross validation technique is employed with different classifiers—Fishers Linear Discriminant Analysis, Random forest, and Support vector machine after filtering the optimal set of features by utilizing stepwise logistic regression method. The best performance achieved by the introduced approach in terms of an area under the receiver operating characteristic (ROC) curve (A_z value) and accuracy (A_cc) are 0.94 and 86.90%, respectively for the mini-MIAS dataset while the same for the DDSM dataset are 0.89 and 80.76%, respectively. The competitive nature of the proposed scheme is evident by comparing the obtained results with schemes in the state-of-the-arts.

本文提出了局部光度属性（LPA），用于将乳腺X线摄影肿块表征为良性或恶性。LPA在光密度图像上测量局部信息，该图像可抑制背景区域并提供有关块状病变的更多详细信息。通过结合两个基准数据库的乳腺X线照片对mini-MIAS和DDSM进行评估，其中，十进制交叉验证技术与不同的分类器一起使用，Fishers线性判别分析，随机森林和过滤后的支持向量机利用逐步逻辑回归方法获得最佳特征集。在接收器工作特性（ROC）曲线下的面积（A _zmini-MIAS数据集的准确度（A _{c c}值）分别为0.94和86.90％，而DDSM数据集的准确度（A _{c c}）分别为0.89和80.76％。通过将获得的结果与现有技术中的方案进行比较，可以看出该方案的竞争性质。