Yu S, Guan L
School of Electrical and Information Engineering, University of Sydney, NSW, Australia.
IEEE Trans Med Imaging. 2000 Feb;19(2):115-26. doi: 10.1109/42.836371.
Clusters of microcalcifications in mammograms are an important early sign of breast cancer. This paper presents a computer-aided diagnosis (CAD) system for the automatic detection of clustered microcalcifications in digitized mammograms. The proposed system consists of two main steps. First, potential microcalcification pixels in the mammograms are segmented out by using mixed features consisting of wavelet features and gray level statistical features, and labeled into potential individual microcalcification objects by their spatial connectivity. Second, individual microcalcifications are detected by using a set of 31 features extracted from the potential individual microcalcification objects. The discriminatory power of these features is analyzed using general regression neural networks via sequential forward and sequential backward selection methods. The classifiers used in these two steps are both multilayer feedforward neural networks. The method is applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications. A free-response operating characteristics (FROC) curve is used to evaluate the performance. Results show that the proposed system gives quite satisfactory detection performance. In particular, a 90% mean true positive detection rate is achieved at the cost of 0.5 false positive per image when mixed features are used in the first step and 15 features selected by the sequential backward selection method are used in the second step. However, we must be cautious when interpreting the results, since the 20 training samples are also used in the testing step.
乳房X光片中的微钙化簇是乳腺癌的一个重要早期迹象。本文提出了一种计算机辅助诊断（CAD）系统，用于自动检测数字化乳房X光片中的簇状微钙化。所提出的系统包括两个主要步骤。首先，利用由小波特征和灰度统计特征组成的混合特征，分割出乳房X光片中潜在的微钙化像素，并根据其空间连通性将其标记为潜在的单个微钙化对象。其次，通过使用从潜在的单个微钙化对象中提取的一组31个特征来检测单个微钙化。使用通用回归神经网络通过顺序向前和顺序向后选择方法分析这些特征的判别能力。这两个步骤中使用的分类器都是多层前馈神经网络。该方法应用于一个包含105个微钙化簇的40幅乳房X光片数据库（奈梅亨数据库）。使用自由响应操作特征（FROC）曲线来评估性能。结果表明，所提出的系统给出了相当令人满意的检测性能。特别是，当第一步使用混合特征且第二步使用通过顺序向后选择方法选择的15个特征时，以每幅图像0.5个假阳性为代价实现了90%的平均真阳性检测率。然而，在解释结果时我们必须谨慎，因为测试步骤中也使用了20个训练样本。
