Computer-aided detection of breast masses on full field digital mammograms.
Computer-aided detection of breast masses on full field digital mammograms.
复制标题
DOI：
10.1118/1.1997327
复制
发表时间：
2005-09
期刊：
影响因子：
3.8
通讯作者：
Jun Wei;B. Sahiner;Lubomir M. Hadjiiski;H. Chan;N. Petrick;M. Helvie;M. Roubidoux;Jun Ge;Chuan Zhou
中科院分区：
文献类型：
--
作者：
Jun Wei;B. Sahiner;Lubomir M. Hadjiiski;H. Chan;N. Petrick;M. Helvie;M. Roubidoux;Jun Ge;Chuan Zhou
我们正在开发一种计算机辅助检测(CAD)系统，用于在全视野数字乳房X光照相(FFDM)图像上进行乳房肿块检测。为了开发一个独立于FFDM制造商专有的预处理方法的CAD系统，我们使用原始的FFDM图像作为输入，并开发了一种用于图像增强的多分辨率预处理方案。提出了一种结合梯度场分析和灰度信息的两阶段预筛选方法，用于在处理后的图像上识别大量候选对象。通过基于聚类的区域生长提取每个识别区域中的可疑结构。对每个可疑目标提取形态和空间灰度相关纹理特征。采用单纯形优化的逐步线性判别分析(LDA)选择最有用的特征。最后，设计了基于规则和LDA的分类器来区分肿块和正常组织。收集了两个数据集：包含110例总共220张图像的双视乳房X线片的海量数据集，以及包含90例总共180张图像的双视乳房X线片的非肿块数据集。所有病例均采用GE SenogRaphe 2000D FFDM机采集。肿块的真实位置由一位经验丰富的放射科医生确定。采用自由响应接收器工作特性分析来评估CAD系统的性能。结果表明，在海量数据集上，我们的CAD系统在0.72、1.08和1.82个假阳性(FP)标记/图像上获得了70%、80%和90%的基于案例的灵敏度。在相应的灵敏度下，无质量数据集上的FP率分别为0.85、1.31和2.14 FP标记/图像。这项研究证明了我们的CAD技术在FFDM图像上自动检测肿块的有效性。
We are developing a computer-aided detection (CAD) system for breast masses on full field digital mammographic (FFDM) images. To develop a CAD system that is independent of the FFDM manufacturer's proprietary preprocessing methods, we used the raw FFDM image as input and developed a multiresolution preprocessing scheme for image enhancement. A two-stage prescreening method that combines gradient field analysis with gray level information was developed to identify mass candidates on the processed images. The suspicious structure in each identified region was extracted by clustering-based region growing. Morphological and spatial gray-level dependence texture features were extracted for each suspicious object. Stepwise linear discriminant analysis (LDA) with simplex optimization was used to select the most useful features. Finally, rule-based and LDA classifiers were designed to differentiate masses from normal tissues. Two data sets were collected: a mass data set containing 110 cases of two-view mammograms with a total of 220 images, and a no-mass data set containing 90 cases of two-view mammograms with a total of 180 images. All cases were acquired with a GE Senographe 2000D FFDM system. The true locations of the masses were identified by an experienced radiologist. Free-response receiver operating characteristic analysis was used to evaluate the performance of the CAD system. It was found that our CAD system achieved a case-based sensitivity of 70%, 80%, and 90% at 0.72, 1.08, and 1.82 false positive (FP) marks/image on the mass data set. The FP rates on the no-mass data set were 0.85, 1.31, and 2.14 FP marks/image, respectively, at the corresponding sensitivities. This study demonstrated the usefulness of our CAD techniques for automated detection of masses on FFDM images.