Mammographic mass classification according to Bi-RADS lexicon
Mammographic mass classification according to Bi-RADS lexicon
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DOI：
10.1049/iet-cvi.2016.0244
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发表时间：
2017-04-01
影响因子：
1.7
通讯作者：
Farida, Merouani Hayet
中科院分区：
文献类型：
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作者：
Chokri, Ferkous;Farida, Merouani Hayet
本研究的目的是提出一种计算机辅助诊断系统，以区分数字化乳房 X 光照片中的四种乳腺成像报告和数据系统 (Bi-RADS) 类别。该系统的灵感来自医生在放射检查期间的方法，正如 BI-RADS 中所商定的那样，其中肿块通过其形状、边界和密度进行描述。作者方法中的质量分割是手动的，因为假设检测已经完成。当分割区域可用时，就可以进行特征提取过程。根据形状、边缘和纹理属性自动计算 22 种视觉特征；这项研究仅使用了一项人类特征，即患者的年龄。最终使用多层感知器根据两种不同的方案完成分类；第一个包含分类质量，以区分四个 BI-RADS 类别（2、3、4 和 5）。在第二篇文章中，作者将异常分为两类（良性和恶性）。所提出的方法已对从数字数据库中提取的 480 个乳房 X 线摄影肿块进行了评估，用于筛查乳房 X 线摄影，所获得的结果令人鼓舞。
The goal of this study is to propose a computer-aided diagnosis system to differentiate between four breast imaging reporting and data system (Bi-RADS) classes in digitised mammograms. This system is inspired by the approach of the doctor during the radiologic examination as it was agreed in BI-RADS, where masses are described by their form, their boundary and their density. The segmentation of masses in the authors' approach is manual because it is supposed that the detection is already made. When the segmented region is available, the features extraction process can be carried out. 22 visual characteristics are automatically computed from shape, edge and textural properties; only one human feature is used in this study, which is the patient's age. Classification is finally done using a multi-layer perceptron according to two separate schemes; the first one consists of classify masses to distinguish between the four BI-RADS classes (2, 3, 4 and 5). In the second one the authors classify abnormalities on two classes (benign and malign). The proposed approach has been evaluated on 480 mammographic masses extracted from the digital database for screening mammography, and the obtained results are encouraging.