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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.7 no.30 >Computer-aided detection of clustered microcalcifications on full-field digital mammograms: A two-view information fusion scheme for FP reduction
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Computer-aided detection of clustered microcalcifications on full-field digital mammograms: A two-view information fusion scheme for FP reduction

机译：全场数字乳房X线照片上群集微钙化的计算机辅助检测：减少FP的两视图信息融合方案

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We are developing new techniques to improve the performance of our computer-aided detection (CAD) system for clustered microcalcifications on full-field digital mammograms (FFDMs). In this study, we designed an information fusion scheme by using joint two-view information on craniocaudal (CC) and mediolateral-oblique (MLO) views. After cluster candidates were detected using a single-view detection technique, candidates on CC and MLO views were paired using their geometrical information. Candidate pairs were classified as true and false pairs with a similarity classifier that used the joint information from both views. Each cluster candidate was also characterized by its single-view features. The outputs of the similarity classifier and the single-view classifier were fused and the cluster candidate was classified as a true microcalcification cluster or a false-positive (FP) using the fused two-view information. A data set of 192 FFDM images was collected from 96 patients at the University of Michigan. All patients had two mamrnographic views. This data set contained 96 microcalcification clusters, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. For training and testing the classifiers, the data set was partitioned into two independent subsets with the malignant cases equally distributed to the two subsets. One subset was used for training and the other subset was used for testing. We compared three computerized methods for geometrically pairing cluster candidates on two mamrnographic views. The areas under the fitted ROC curves were 0.75±0.01, 0.74±0.01, and 0.76±0.01 for the three methods, respectively. The difference between any two methods measured by the area under the fitted ROC curve, Az, was not statistically significant (p > 0.05). We also evaluated a new hybrid pairing scheme that used two different sensitivity levels for defining cluster pairs based on the single-view scores. The single-view CAD system achieved cluster-based sensitivities of 75%, 80%, and 85% at 0.48, 0.86, and 1.05 FPs/image, respectively. The joint two-view CAD system achieved the same sensitivity levels at 0.29, 0.46, and 0.89 FPs/image. When the hybrid pairing was used in the joint two-view CAD system, the same cluster-based sensitivities were achieved at 0.26, 0.37, and 0.88 FPs/image. Our results indicate that correspondence of cluster candidates on two different views provides valuable additional information for distinguishing FPs from true microcalcification clusters.
机译：我们正在开发新的技术，以改善计算机辅助检测（CAD）系统在全场数字乳房X线照片（FFDM）上的集群微钙化的性能。在这项研究中，我们设计了一种信息融合方案，该方案采用了关于颅尾（CC）和中外侧斜（MLO）的联合两视图信息。使用单视图检测技术检测到候选聚类后，使用其几何信息将CC和MLO视图上的候选配对。候选对通过使用两个视图中的联合信息的相似性分类器分类为真对假。每个候选群集还具有其单视图功能。融合相似度分类器和单视图分类器的输出，并使用融合的两视图信息将候选聚类分类为真正的微钙化聚类或假阳性（FP）。从密歇根大学的96位患者中收集了192张FFDM图像的数据集。所有患者都有两次乳房X线摄影检查。该数据集包含96个微钙化簇，其中28个簇经活检证实为恶性，而68个为良性。为了训练和测试分类器，将数据集划分为两个独立的子集，恶性病例平均分配到这两个子集。一个子集用于训练，另一子集用于测试。我们比较了两种在乳腺X线摄影视图上对候选群集进行几何配对的计算机化方法。三种方法的拟合ROC曲线下面积分别为0.75±0.01、0.74±0.01和0.76±0.01。通过拟合ROC曲线下面积Az测得的任何两种方法之间的差异均无统计学意义（p> 0.05）。我们还评估了一种新的混合配对方案，该方案使用两个不同的敏感度级别基于单视图得分定义聚类对。单视图CAD系统在0.48、0.86和1.05 FPs /图像下分别实现了基于聚类的敏感度，分别为75％，80％和85％。联合双视图CAD系统在0.29、0.46和0.89 FP /图像时达到了相同的灵敏度水平。当在联合双视图CAD系统中使用混合配对时，在0.26、0.37和0.88 FP /图像时可获得相同的基于簇的灵敏度。我们的结果表明，候选簇在两个不同视图上的对应关系为区分FP与真正的微钙化簇提供了有价值的附加信息。

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