Valencia-Hernandez I, Peregrina-Barreto H, Reyes-Garcia C A, Lopez-Armas G C
Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Santa Maria Tonantzintla, Puebla 72840, México.
Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Santa Maria Tonantzintla, Puebla 72840, México.
Comput Methods Programs Biomed. 2021 Mar;200:105825. doi: 10.1016/j.cmpb.2020.105825. Epub 2020 Nov 5.
Mammographic density (MD) is conformed by a different percentage of stromal, epithelial, and adipose tissue within the breast. One of the most critical findings in mammographic patterns for establishing a diagnosis of breast cancer is high breast tissue density. There is a wide variety of works focused on the study and automatic calculation of general breast density; however, they do not provide more detailed information about the changes that may occur within the breast tissue. This work proposes to generate a breast density map based on a texture analysis to identify the internal composition and distribution of the breast tissue through the diffuse division technique of the different densities inside the breast. Therefore, it is possible to obtain a density map associated with the breast that allows us to distinguish and quantify the different types of breast densities and their distribution according to the Breast Imaging Reporting and Data System (BI-RADS Breast Density Category). The proposed methodology was tested with mammograms from the BCDR and InBreast databases, demonstrating consistency in results and reaching an accuracy of 84.2% and 81.3%, respectively. Finally, the information obtained from the density map and its analysis could be a support tool for the specialist physician to monitor changes in breast density over time, since the fuzzy classification carried out allows quantifying the degree of membership in the BI-RADS breast density classes.
乳腺钼靶密度（MD）由乳腺内不同比例的基质、上皮和脂肪组织构成。在乳腺钼靶图像模式中，用于诊断乳腺癌的最关键发现之一是乳腺组织密度高。有大量工作专注于乳腺总体密度的研究和自动计算；然而，它们并未提供关于乳腺组织内可能发生的变化的更详细信息。这项工作提出基于纹理分析生成乳腺密度图，通过乳腺内不同密度的扩散分割技术来识别乳腺组织的内部组成和分布。因此，有可能获得与乳腺相关的密度图，使我们能够根据乳腺影像报告和数据系统（BI-RADS乳腺密度分类）区分和量化不同类型的乳腺密度及其分布。所提出的方法在BCDR和InBreast数据库的乳腺钼靶图像上进行了测试，结果显示出一致性，准确率分别达到84.2%和81.3%。最后，从密度图及其分析中获得的信息可以成为专科医生监测乳腺密度随时间变化的辅助工具，因为所进行的模糊分类能够量化在BI-RADS乳腺密度类别中的隶属度。
