Narváez Fabián, Alvarez Jorge, Garcia-Arteaga Juan D, Tarquino Jonathan, Romero Eduardo
Computer Imaging and Medical Applications Laboratory - Cim@lab, Faculty of Medicine - Universidad Nacional de Colombia, Carrera 30 No 45-03, Bogotá, DC, Colombia.
J Med Syst. 2017 Feb;41(2):26. doi: 10.1007/s10916-016-0672-5. Epub 2016 Dec 22.
Architectural distortion (AD) is a common cause of false-negatives in mammograms. This lesion usually consists of a central retraction of the connective tissue and a spiculated pattern radiating from it. This pattern is difficult to detect due the complex superposition of breast tissue. This paper presents a novel AD characterization by representing the linear saliency in mammography Regions of Interest (ROI) as a graph composed of nodes corresponding to locations along the ROI boundary and edges with a weight proportional to the line intensity integrals along the path connecting any pair of nodes. A set of eigenvectors from the adjacency matrix is then used to extract discriminant coefficients that represent those nodes with higher salient lines. A dimensionality reduction is further accomplished by selecting the pair of nodes with major contribution for each of the computed eigenvectors. The set of main salient lines is then assembled as a feature vector that inputs a conventional Support Vector Machine (SVM). Experimental results with two benchmark databases, the mini-MIAS and DDSM databases, demonstrate that the proposed linear saliency domain method (LSD) performs well in terms of accuracy. The approach was evaluated with a set of 246 RoI extracted from the DDSM (123 normal tissues and 123 AD) and a set of 38 ROI from the mini-MIAS collections (19 normal tissues and 19 AD) respectively. The classification results showed respectively for both databases an accuracy rate of 89 % and 87 %, a sensitivity rate of 85 % and 95 %, and a specificity rate of 93 % and 84 %. Likewise, the area under curve (A ) of the Receiver Operating Characteristic (ROC) curve was 0.93 for both databases.
乳腺结构扭曲（AD）是乳腺钼靶检查中假阴性结果的常见原因。这种病变通常由结缔组织的中央回缩以及从其辐射出的毛刺状形态组成。由于乳腺组织的复杂叠加，这种形态很难被检测到。本文提出了一种新的AD特征描述方法，通过将乳腺钼靶感兴趣区域（ROI）中的线性显著性表示为一个图，该图由对应于ROI边界位置的节点以及权重与连接任意一对节点的路径上的线强度积分成比例的边组成。然后，使用邻接矩阵的一组特征向量来提取代表那些具有较高显著性线条的节点的判别系数。通过为每个计算出的特征向量选择贡献最大的一对节点，进一步实现降维。然后将主要显著性线条集组装成一个特征向量，输入到传统的支持向量机（SVM）中。使用两个基准数据库（mini-MIAS和DDSM数据库）进行的实验结果表明，所提出的线性显著性域方法（LSD）在准确性方面表现良好。该方法分别使用从DDSM中提取的一组246个ROI（123个正常组织和123个AD）以及从mini-MIAS数据集中提取的一组38个ROI（19个正常组织和19个AD）进行评估。对于这两个数据库，分类结果分别显示准确率为89%和87%，灵敏度为85%和95%，特异性为93%和84%。同样，两个数据库的接收器操作特征（ROC）曲线下面积（AUC）均为0.93。
