Wu Yi-Ta, Wei Jun, Hadjiiski Lubomir M, Sahiner Berkman, Zhou Chuan, Ge Jun, Shi Jiazheng, Zhang Yiheng, Chan Heang-Ping
Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA.
Med Phys. 2007 Aug;34(8):3334-44. doi: 10.1118/1.2756612.
We have developed a false positive (FP) reduction method based on analysis of bilateral mammograms for computerized mass detection systems. The mass candidates on each view were first detected by our unilateral computer-aided detection (CAD) system. For each detected object, a regional registration technique was used to define a region of interest (ROI) that is "symmetrical" to the object location on the contralateral mammogram. Texture features derived from the spatial gray level dependence matrices and morphological features were extracted from the ROI containing the detected object on a mammogram and its corresponding ROI on the contralateral mammogram. Bilateral features were then generated from corresponding pairs of unilateral features for each object. Two linear discriminant analysis (LDA) classifiers were trained from the unilateral and the bilateral feature spaces, respectively. Finally, the scores from the unilateral LDA classifier and the bilateral LDA asymmetry classifier were fused with a third LDA whose output score was used to distinguish true mass from FPs. A data set of 341 cases of bilateral two-view mammograms was used in this study, of which 276 cases with 552 bilateral pairs contained 110 malignant and 166 benign biopsy-proven masses and 65 cases with 130 bilateral pairs were normal. The mass data set was divided into two subsets for twofold cross-validation training and testing. The normal data set was used for estimation of FP rates. It was found that our bilateral CAD system achieved a case-based sensitivity of 70%, 80%, and 85% at average FP rates of 0.35, 0.75, and 0.95 FPs/image, respectively, on the test data sets with malignant masses. In comparison to the average FP rates for the unilateral CAD system of 0.58, 1.33, and 1.63, respectively, at the corresponding sensitivities, the FP rates were reduced by 40%, 44%, and 42% with the bilateral symmetry information. The improvement was statistically significance (p < 0.05) as estimated by JAFROC analysis.
我们基于对双侧乳腺钼靶图像的分析，为计算机化肿块检测系统开发了一种减少假阳性（FP）的方法。首先通过我们的单侧计算机辅助检测（CAD）系统检测每个视图上的肿块候选区域。对于每个检测到的对象，使用区域配准技术来定义一个感兴趣区域（ROI），该区域与对侧乳腺钼靶图像上对象位置“对称”。从包含乳腺钼靶图像上检测到的对象及其对侧乳腺钼靶图像上相应ROI的区域中提取源自空间灰度依赖矩阵的纹理特征和形态特征。然后为每个对象从相应的单侧特征对生成双侧特征。分别从单侧和双侧特征空间训练两个线性判别分析（LDA）分类器。最后，将单侧LDA分类器和双侧LDA不对称分类器的分数与第三个LDA融合，其输出分数用于区分真正的肿块和假阳性。本研究使用了一个包含341例双侧双视图乳腺钼靶图像的数据集，其中276例（552对双侧图像）包含110个经活检证实的恶性肿块和166个良性肿块，65例（130对双侧图像）为正常。肿块数据集被分为两个子集用于双重交叉验证训练和测试。正常数据集用于估计假阳性率。结果发现，我们的双侧CAD系统在含有恶性肿块的测试数据集上，平均假阳性率分别为0.35、0.75和0.95个假阳性/图像时，基于病例的敏感性分别达到70%、80%和85%。与单侧CAD系统在相应敏感性下分别为0.58、1.33和1.63的平均假阳性率相比，利用双侧对称信息后假阳性率分别降低了40%、44%和42%。经JAFROC分析估计，这种改善具有统计学意义（p < 0.05）。
