Breast cancer is the most common cancer diagnosed in women worldwide. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG)-based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59, 1.78, and 0.68 average false positives per image on Digital Database for Screening Mammography (scanned film), INbreast (direct radiography) database, and PGIMER-IITKGP mammogram (direct radiography) database, respectively. Thus, it might be used as a second reader as well as a screening tool to reduce the burden on radiologists.

中文翻译：

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用于检测乳房X线照片中微钙化簇的筛查CAD工具。

乳腺癌是全世界女性中最常见的癌症。仅通过乳房X线照片中的微钙化簇就可以检测出多达50％的不可触及的乳腺癌。本文提出了一种新颖且完全自动化的算法，用于检测乳房X光照片中的微钙化簇。提出了一种多尺度二维非线性能量算子，以增强微钙化与背景之间的对比度。基于取向梯度（HOG）的特征的几种纹理，形状，强度和直方图用于区分微钙化与其他较亮的乳房X线照片区域。提出了一种新的基于数据分布的多数类数据约简技术，以解决数据不平衡问题。该算法能够以2.59、1.78和0达到100％的灵敏度。在用于乳腺X射线摄影（扫描胶片）的数字数据库，Inbreast（直接X射线摄影）数据库和PGIMER-IITKGP乳腺X线照片（直接X射线摄影）数据库上，每个图像分别有68个平均假阳性。因此，它可以用作第二阅读器以及筛选工具，以减轻放射科医生的负担。