A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
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DOI：
10.1155/2020/7695207
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发表时间：
2020-05-11
影响因子：
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通讯作者：
Ma, Xiaohong
中科院分区：
文献类型：
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作者：
Boumaraf, Said;Liu, Xiabi;Ma, Xiaohong
乳房X线摄影术仍然是早期乳腺癌筛查最普遍的成像工具。乳腺X线摄影报告中用于描述异常的语言基于乳腺成像报告和数据系统（BI-RADS）。将正确的BI-RADS分类用于每个检查的乳房X光片，即使对于专家来说也是一项艰巨而具有挑战性的任务。本文提出了一种新的和有效的计算机辅助诊断（CAD）系统分类乳腺摄影肿块到四个评估类别的BI-RADS。首先对肿块区域进行直方图均衡化增强，然后基于区域生长技术进行半自动分割。然后从每个肿块的形状、边缘和密度以及肿块大小和患者年龄中提取总共130个手工制作的BI-RADS特征，如BI-RADS乳房X光检查中所述。然后，提出了一种基于遗传算法（GA）的改进的特征选择方法，以选择最具临床意义的BI-RADS特征。最后，一个反向传播神经网络（BPN）的分类，其精度被用来作为遗传算法的适应度。使用来自筛查性乳腺X线摄影（DDSM）数字数据库的一组500张乳腺X线摄影图像进行评价。我们的系统实现了分类准确率，阳性预测值，阴性预测值和马修斯相关系数分别为84.5%，84.4%，94.8%和79.3%。据我们所知，这是目前乳腺X射线摄影中乳腺肿块BI-RADS分类的最佳结果，这使得所提出的系统有望支持放射科医生根据自动分配的BI-RADS类别决定适当的患者管理。
Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.