A new approach to develop computer-aided detection schemes of digital mammograms.
A new approach to develop computer-aided detection schemes of digital mammograms.
复制标题
DOI：
10.1088/0031-9155/60/11/4413
复制
发表时间：
2015-06-07
影响因子：
3.5
通讯作者：
Zheng B
中科院分区：
文献类型：
--
作者：
Tan M;Qian W;Pu J;Liu H;Zheng B
本研究的目的是开发一种基于计算机辅助检测（CAD）方案的新的全局乳腺X线图像特征分析，并评估其在检测阳性筛查乳腺X线检查中的性能。本研究使用的数据集包含从 1896 年全视野数字乳房 X 线摄影 (FFDM) 筛查检查中获取的图像。其中，812例癌症呈阳性，1084例呈阴性或良性。分割乳房区域后，应用计算机化方案计算头尾 (CC) 和内侧倾斜 (MLO) 视图的四张乳房 X 光照片中每张的 92 个基于全局乳房 X 光照相组织密度的特征。将三个现有的流行风险因素（女性年龄、主观评价的乳房X线密度和乳腺癌家族史）添加到初始特征池中后，我们应用顺序前向浮动选择（SFFS）特征选择算法分别从双边CC和MLO视图图像中选择相关特征。所选的 CC 和 MLO 视图图像特征用于训练两个人工神经网络 (ANN)。然后，第三个 ANN 将结果融合起来，构建一个两阶段分类器，以预测 FFDM 筛查检查呈阳性的可能性。使用十倍交叉验证方法测试 CAD 性能。受试者工作特征曲线下的计算面积为 AUC=0.779±0.025，随着 CAD 生成的检测分数的增加，优势比从 1 单调增加到 31.55。研究表明，这种新的基于全局图像特征的 CAD 方案具有相对较高的辨别能力，可以提示 FFDM 检查呈阳性的高风险，这可能为辅助放射科医生阅读和解释筛查性乳房 X 光检查提供一种新的 CAD 提示方法。
The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography examinations. A dataset that includes images acquired from 1896 full-field digital mammography (FFDM) screening examinations was used in this study. Among them, 812 cases were positive for cancer and 1084 were negative or benign. After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral oblique (MLO) views. After adding three existing popular risk factors (woman’s age, subjectively rated mammographic density, and family breast cancer history) into the initial feature pool, we applied a Sequential Forward Floating Selection (SFFS) feature selection algorithm to select relevant features from the bilateral CC and MLO view images separately. The selected CC and MLO view image features were used to train two artificial neural networks (ANNs). The results were then fused by a third ANN to build a two-stage classifier to predict the likelihood of the FFDM screening examination being positive. CAD performance was tested using a ten-fold cross-validation method. The computed area under the receiver operating characteristic curve was AUC=0.779±0.025 and the odds ratio monotonically increased from 1 to 31.55 as CAD-generated detection scores increased. The study demonstrated that this new global image feature based CAD scheme had a relatively higher discriminatory power to cue the FFDM examinations with high risk of being positive, which may provide a new CAD-cueing method to assist radiologists in reading and interpreting screening mammograms.
登录
查看更多内容
影响因子：
39.2
作者：
Hubbard RA;Kerlikowske K;Flowers CI;Yankaskas BC;Zhu W;Miglioretti DL
通讯作者：
Miglioretti DL
影响因子：
158.5
作者：
Kriege, M;Brekelmans, CTM;Klijn, JGM
通讯作者：
Klijn, JGM
影响因子：
5
作者：
Fenton, JJ;Egger, J;Elmore, JG
通讯作者：
Elmore, JG
影响因子：
64.8
作者：
RUMELHART, DE;HINTON, GE;WILLIAMS, RJ
通讯作者：
WILLIAMS, RJ
DOI：
10.1093/jnci/88.10.643
发表时间：
1996-05-15
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
Laya, MB;Larson, EB;White, E
通讯作者：
White, E