Abstract：
Mass detection in mammograms has a limited approach to the presence of a mass in overlapping denser fibroglandular breast regions. In addition, various breast density levels could decrease the learning system\'s ability to extract sufficient feature descriptors and may result in lower accuracy performance. Therefore, this study is proposing a textural-based image enhancement technique named Spatial-based Breast Density Enhancement for Mass Detection (SbBDEM) to boost textural features of the overlapped mass region based on the breast density level. This approach determines the optimal exposure threshold of the images\' lower contrast limit and optimizes the parameters by selecting the best intensity factor guided by the best Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE) scores separately for both dense and non-dense breast classes prior to training. Meanwhile, a modified You Only Look Once v3 (YOLOv3) architecture is employed for mass detection by specifically assigning an extra number of higher-valued anchor boxes to the shallower detection head using the enhanced image. The experimental results show that the use of SbBDEM prior to training mass detection promotes superior performance with an increase in mean Average Precision (mAP) of 17.24% improvement over the non-enhanced trained image for mass detection, mass segmentation of 94.41% accuracy, and 96% accuracy for benign and malignant mass classification. Enhancing the mammogram images based on breast density is proven to increase the overall system\'s performance and can aid in an improved clinical diagnosis process.
摘要：
乳房X线照片中的质量检测对于在重叠的较致密的纤维腺体乳房区域中存在质量的方法有限。此外,不同的乳腺密度水平可能会降低学习系统提取足够的特征描述符的能力，并可能导致较低的准确性。因此,这项研究提出了一种基于纹理的图像增强技术，称为基于空间的乳腺密度增强肿块检测（SbBDEM），以基于乳腺密度水平增强重叠肿块区域的纹理特征。此方法确定图像的最佳曝光阈值对比度下限，并通过选择最佳强度因子来优化参数，该强度因子由分别针对密集和非密集乳房类别的最佳盲/无参考图像空间质量评估器（BRISQUE）评分指导在训练之前。同时,修改后的您只看一次v3（YOLOv3）架构用于质量检测，通过使用增强的图像专门为较浅的检测头分配额外数量的较高价值的锚盒。实验结果表明，在训练质量检测之前使用SbBDEM可以提高平均平均精度（mAP），比未增强的训练图像提高17.24％的质量检测性能，质量分割，准确率为94.41%，良性和恶性肿块分类的准确率为96%。根据乳房密度增强乳房X线照片图像被证明可以提高整个系统的性能，并有助于改进临床诊断过程。
