Sino-Dutch Biomedical and Information Engineering School, Northeastern University, China.
Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., China.
J Xray Sci Technol. 2019;27(2):321-342. doi: 10.3233/XST-180461.
The morbidity of breast cancer has been increased in these years and ranked the first of all female diseases. Computer-aided diagnosis techniques for mammograms can help radiologists find early breast lesions. In mammograms, the degree of malignancy of the tumor is not only related to its morphology and texture features, but also closely related to the density of the tumor. However, in the current research on breast masses detection and diagnosis, people usually use the fusion feature of morphology and texture but neglect density, or only the density feature is considered. Therefore, this paper proposes a method to detect and diagnose the breast mass using fused features with density.
In this paper, we first propose a method based on sub-region clustering to detect the breast mass. The breast region is divided into sub-regions of equal size, and each sub-region is extracted based on local density feature, after that, an Unsupervised ELM (US-ELM) is used for clustering to complete the mass detection. Second, the feature model is constructed based on the mass. This model is composed of the mass region density feature, morphology feature and texture feature. And Genetic Algorithm is used for feature selection, and the optimized feature model is formed. Finally, ELM is used to diagnose benign or malignant mass.
An experiment on the real dataset of 480 mammograms in Northeast China shows that our proposed method can effectively improve the detection and diagnosis accuracy of breast masses, where we obtained 0.9184 precision in detection of breast masses and 0.911 accuracy in diagnosis of breast masses.
We have proposed a mass detection system, which achieves better detection accuracy performance than the existing state-of-art algorithm. We also propose a mass diagnosis system based on the fused features with density, which is more efficient than other feature model and classifier on the same dataset.
近年来，乳腺癌的发病率有所增加，已位居女性疾病之首。乳腺 X 线摄影的计算机辅助诊断技术可以帮助放射科医生发现早期乳腺病变。在乳腺 X 线摄影中，肿瘤的恶性程度不仅与形态和纹理特征有关，而且与肿瘤的密度密切相关。然而，在目前对乳腺肿块的检测和诊断的研究中，人们通常使用形态和纹理的融合特征，但忽略了密度，或者只考虑了密度特征。因此，本文提出了一种利用密度融合特征检测和诊断乳腺肿块的方法。
本文首先提出了一种基于子区域聚类的乳腺肿块检测方法。将乳腺区域划分为大小相等的子区域，然后基于局部密度特征提取每个子区域，之后使用无监督极限学习机（US-ELM）进行聚类，完成肿块检测。其次，基于肿块构建特征模型。该模型由肿块区域密度特征、形态特征和纹理特征组成。并使用遗传算法进行特征选择，形成优化的特征模型。最后，使用极限学习机（ELM）对良性或恶性肿块进行诊断。
在东北地区 480 张乳腺 X 线摄影的真实数据集上进行的实验表明，所提出的方法可以有效提高乳腺肿块的检测和诊断精度，其中肿块检测的精度为 0.9184，肿块诊断的准确率为 0.911。
本文提出了一种肿块检测系统，其检测精度优于现有最先进的算法。还提出了一种基于密度融合特征的肿块诊断系统，在相同数据集上，该系统比其他特征模型和分类器更高效。
