论文标题
根据解剖学特征自动消除乳房X线照片中的胸肌
Automatic elimination of the pectoral muscle in mammograms based on anatomical features
论文作者
论文摘要
数字乳房X线照片检查是早期发现人类乳腺组织异常的最流行技术。当通过计算方法分析乳房X线照片时，胸肌的存在可能会影响乳腺病变检测的结果。在中外侧倾斜视图（MLO）中，这个问题尤为明显，胸肌占据乳房X线摄影的很大一部分。因此，识别和消除胸肌是改善乳房组织自动歧视的重要步骤。在本文中，我们提出了一种基于解剖特征来解决此问题的方法。我们的方法由两个步骤组成：（1）删除嘈杂元素（例如标签，标记，划痕和楔子）的过程，以及（2）基于Beta分布的强度转换应用。通过来自乳房X线图分析社会（MINI-MIA）数据库的322次数字乳房X线照片对新方法进行测试，并通过一组84个乳房X线照片，先前已经计算出该面积归一化误差。结果表明该方法的性能非常好。
Digital mammogram inspection is the most popular technique for early detection of abnormalities in human breast tissue. When mammograms are analyzed through a computational method, the presence of the pectoral muscle might affect the results of breast lesions detection. This problem is particularly evident in the mediolateral oblique view (MLO), where pectoral muscle occupies a large part of the mammography. Therefore, identifying and eliminating the pectoral muscle are essential steps for improving the automatic discrimination of breast tissue. In this paper, we propose an approach based on anatomical features to tackle this problem. Our method consists of two steps: (1) a process to remove the noisy elements such as labels, markers, scratches and wedges, and (2) application of an intensity transformation based on the Beta distribution. The novel methodology is tested with 322 digital mammograms from the Mammographic Image Analysis Society (mini-MIAS) database and with a set of 84 mammograms for which the area normalized error was previously calculated. The results show a very good performance of the method.
