Computer-aided characterization of mammographic masses: Accuracy of mass segmentation and its effects on characterization
Computer-aided characterization of mammographic masses: Accuracy of mass segmentation and its effects on characterization
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DOI：
10.1109/42.974922
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发表时间：
2001-12-01
影响因子：
10.6
通讯作者：
Gurcan, MN
中科院分区：
文献类型：
--
作者：
Sahiner, B;Petrick, N;Gurcan, MN
在许多计算机辅助诊断(CAD)系统中，肿块分割被用作对乳腺肿块进行良恶性分类的第一步。本文的目的是研究我们实验室开发的一种自动肿块分割方法的精度，并考察分割阶段对整体分类精度的影响。自动分割方法与由两位专业放射科医生(111和112)在100个肿块的数据集上使用三个相似性或距离度量进行的手动分割进行了定量比较。R1和R2、计算机和R1以及计算机和R2之间的面积重叠量分别为0.76+/-0.13、0.74+/-0.11和0.74+/-0.13。将两位放射科医生在这些测量上的观察者间差异与计算机和放射科医生之间的相应差异进行比较。使用三个相似性度量和来自两个放射科医生的数据，总共进行了六个统计检验。仅在一次测试中，计算机分割和放射科医生分割之间的差异明显大于观察者间的可变性。从102名患者的249张胶片的数据集中提取了两组纹理、形态和毛刺特征，一组基于计算机分割，另一组基于放射科医生分割。利用这两个特征集对基于逐步特征选择和线性判别分析的分类器进行训练和测试。采用留一病例法进行数据采集。对于基于病例的分类，基于放射科医生分割的特征集和基于计算机分割的特征集的受试者工作特征(ROC)曲线下面积A(X)分别为0.89和0.88。两种ROC曲线之间的差异无统计学意义。
Mass segmentation is used as the first step in many computer-aided diagnosis (CAD) systems for classification of breast masses as malignant or benign. The goal of this paper was to study the accuracy of an automated mass segmentation method developed in our laboratory, and to investigate the effect of the segmentation stage on the overall classification accuracy. The automated segmentation method was quantitatively compared with manual segmentation by two expert radiologists (111 and 112) using three similarity or distance measures on a data set of 100 masses. The area overlap measures between R1 and R2, the computer and R1, and the computer and R2 were 0.76 +/- 0.13, 0.74 +/- 0.11, and 0.74 +/- 0.13, respectively. The interobserver difference in these measures between the two radiologists was compared with the corresponding differences between the computer and the radiologists. Using three similarity measures and data from two radiologists, a total of six statistical tests were performed. The difference between the computer and the radiologist segmentation was significantly larger than the interobserver variability in only one test. Two sets of texture, morphological, and spiculation features, one based on the computer segmentation, and the other based on radiologist segmentation, were extracted from a data set of 249 films from 102 patients. A classifier based on stepwise feature selection and linear discriminant analysis was trained and tested using the two feature sets. The leave-one-case-out method was used for data sampling. For case-based classification, the area A(x) under the receiver operating characteristic (ROC) curve was 0.89 and 0.88 for the feature sets based on the radiologist segmentation and computer segmentation, respectively. The difference between the two ROC curves was not statistically significant.