Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580, São Luís, MA, Brazil.
Pontifical Catholic University of Rio de Janeiro - PUC-Rio, R. São Vicente, 225, Gávea, 22453-900, Rio de Janeiro, RJ, Brazil.
Comput Biol Med. 2015 Feb;57:42-53. doi: 10.1016/j.compbiomed.2014.11.016. Epub 2014 Dec 10.
Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts identify suspicious areas that are difficult to perceive with the human eye, thus aiding in the detection and diagnosis of cancer. This work proposes a methodology for the discrimination and classification of regions extracted from mammograms as mass and non-mass. The Digital Database for Screening Mammography (DDSM) was used in this work for the acquisition of mammograms. The taxonomic diversity index (Δ) and the taxonomic distinctness (Δ(⁎)), which were originally used in ecology, were used to describe the texture of the regions of interest. These indexes were computed based on phylogenetic trees, which were applied to describe the patterns in regions of breast images. Two approaches were used for the analysis of texture: internal and external masks. A support vector machine was used to classify the regions as mass and non-mass. The proposed methodology successfully classified the masses and non-masses, with an average accuracy of 98.88%.
乳腺癌是世界上第二常见的癌症类型。已经有几种计算机辅助检测和诊断系统被用于帮助健康专家识别人眼难以察觉的可疑区域，从而辅助癌症的检测和诊断。这项工作提出了一种用于区分和分类从乳房 X 光片中提取的肿块和非肿块区域的方法。这项工作使用了数字筛查乳房 X 光片数据库（DDSM）来获取乳房 X 光片。最初用于生态学的分类多样性指数（Δ）和分类独特性（Δ(*)）被用于描述感兴趣区域的纹理。这些索引是基于系统发育树计算的，用于描述乳房图像区域的模式。纹理分析使用了两种方法：内部和外部掩模。支持向量机用于对区域进行分类，分为肿块和非肿块。所提出的方法成功地对肿块和非肿块进行了分类，平均准确率为 98.88%。
