Varela Celia, Tahoces Pablo G, Méndez Arturo J, Souto Miguel, Vidal Juan J
Department of Radiology, University of Santiago de Compostela, Complejo Hospitalario de Santiago de Compostela (CHUS), Spain.
Comput Biol Med. 2007 Feb;37(2):214-26. doi: 10.1016/j.compbiomed.2005.12.006. Epub 2006 Apr 18.
We propose a system to detect malignant masses on mammograms. We investigated the behavior of an iris filter at different scales. After iris filter was applied, suspicious regions were segmented by means of an adaptive threshold. Suspected regions were characterized with features based on the iris filter output and, gray level, texture, contour-related, and morphological features extracted from the image. A backpropagation neural network classifier was trained to reduce the number of false positives. The system was developed and evaluated with two completely independent data sets. Results for a test set of 66 malignant and 49 normal cases, evaluated with free-response receiver operating characteristic analysis, yielded a sensitivity of 88% and 94% at 1.02 false positives per image for lesion-based and case-based evaluation, respectively. Results suggest that the proposed method could help radiologists as a second reader in mammographic screening.
我们提出了一种用于检测乳房X光片中恶性肿块的系统。我们研究了虹膜滤波器在不同尺度下的行为。应用虹膜滤波器后，通过自适应阈值分割可疑区域。基于虹膜滤波器输出以及从图像中提取的灰度、纹理、轮廓相关和形态学特征对可疑区域进行特征描述。训练了一个反向传播神经网络分类器以减少假阳性的数量。该系统使用两个完全独立的数据集进行开发和评估。对于一个包含66例恶性病例和49例正常病例的测试集，通过自由响应接收器操作特性分析进行评估，基于病变的评估在每幅图像1.02个假阳性时灵敏度为88%，基于病例的评估在每幅图像1.02个假阳性时灵敏度为94%。结果表明，所提出的方法可以作为乳房X光筛查中的第二阅读者帮助放射科医生。
