Department of Computer Architecture and Technology, IIiA-IdIBGi, University of Girona, Campus Montilivi, Ed. P-IV, 17071 Girona, Spain.
J Digit Imaging. 2010 Oct;23(5):527-37. doi: 10.1007/s10278-009-9217-5. Epub 2009 Jun 9.
Studies reported in the literature indicate that the increase in the breast density is one of the strongest indicators of developing breast cancer. In this paper, we present an approach to automatically evaluate the density of a breast by segmenting its internal parenchyma in either fatty or dense class. Our approach is based on a statistical analysis of each pixel neighbourhood for modelling both tissue types. Therefore, we provide connected density clusters taking the spatial information of the breast into account. With the aim of showing the robustness of our approach, the experiments are performed using two different databases: the well-known Mammographic Image Analysis Society digitised database and a new full-field digital database of mammograms from which we have annotations provided by radiologists. Quantitative and qualitative results show that our approach is able to correctly detect dense breasts, segmenting the tissue type accordingly.
文献报道的研究表明，乳腺密度的增加是乳腺癌发展的最强指标之一。在本文中，我们提出了一种自动评估乳腺密度的方法，通过将其内部实质分割为脂肪或致密类来实现。我们的方法基于对每个像素邻域的统计分析，为两种组织类型建模。因此，我们提供了连接的密度聚类，考虑了乳腺的空间信息。为了展示我们方法的稳健性，实验使用了两个不同的数据库进行：著名的 Mammographic Image Analysis Society 数字化数据库和一个新的全数字化乳腺 X 线摄影数据库，我们从该数据库中获得了放射科医生提供的注释。定量和定性结果表明，我们的方法能够正确地检测致密的乳腺，并相应地分割组织类型。
