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Concordance of Computer-Extracted Image Features with BI-RADS Descriptors for Mammographic Mass Margin

机译:计算机提取的图像特征的一致性与乳房X线图质量裕度的Bi-Rads描述符

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The purpose of this study was to develop and evaluate computer-extracted features for characterizing mammographic mass margins according to BI-RADS spiculated and circumscribed categories. The mass was automatically segmented using an active contour model. A spiculation measure for a pixel on the mass boundary was defined by using the angular difference between the image gradient vector and the normal to the mass, averaged over pixels in a spiculation search region. For the circumscribed margin feature, the angular difference between the principal eigenvector of the Hessian matrix and the normal to the mass was estimated in a band of pixels centered at each point on the boundary, and the feature was extracted from the resulting profile along the boundary. Three MQSA radiologists provided BI-RADS margin ratings for a data set of 198 regions of interest containing breast masses. The features were evaluated with respect to the individual radiologists' characterization using receiver operating characteristic (ROC) analysis, as well as with respect to that from the majority rule, in which a mass was labeled as spiculated (circumscribed) if it was characterized as such by 2 or 3 radiologists, and non-spiculated (non-circumscribed) otherwise. We also investigated the performance of the features for consensus masses, defined as those labeled as spiculated (circumscribed) or non-spiculated (non-circumscribed) by all three radiologists. When masses were labeled according to radiologists R1, R2, and R3 individually, the spiculation feature had an area A_z under the ROC curve of 0.90±0.04, 0.90±0.03, 0.88±0.03, respectively, while the circumscribed margin feature had an A_z value of 0.77±0.04, 0.74±0.04, and 0.80±0.03, respectively. When masses were labeled according to the majority rule, the A_z values for the spiculation and the circumscribed margin features were 0.92±0.03 and 0.80±0.03, respectively. When only the consensus masses were considered, the A_z values for the spiculation and the circumscribed margin features were 0.96±0.04 and 0.87±0.04, respectively. We conclude that the newly developed features had high accuracy for characterizing mass margins according to BI-RADS descriptors.
机译:本研究的目的是开发和评估计算机提取的特征,用于根据Bi-Rads Spiculated和外接类别表征乳房X线肿块边距。使用活动轮廓模型自动分割质量。通过使用图像梯度向量和常规到质量之间的角度差来限定质量边界上的像素的刺激度量,在刺激搜索区域中的像素上平均在像素上的平均值。对于外接的裕度特征,在Hessian矩阵的主要特征段和常规到质量之间的角度差估计在边界上的每个点以像素为中心的像素的条带中,并且沿着边界从得到的轮廓中提取该特征。三位MQSA放射科医生提供了198个含有乳腺菌素的198个兴趣区域的数据集的Bi-RADS边缘评分。使用接收器操作特征(ROC)分析以及来自大多数规则的各个放射科学师的表征来评估该特征,以及从大多数规则的那种情况下,如果其特征在于,将质量标记为刺激(界定)通过2或3位放射科医生,否则是非刺激的(非界定)。我们还调查了共识群众的特征的性能,定义为标记为所有三个放射科学家标记为刺激(外接)或非刺激(非界定)的那些。当根据放射科R1,R2和R3被单独标记质量时,分子特征在ROC曲线下具有0.90±0.04,0.90±0.03,0.88±0.03的区域A_Z,而外接的裕度特征具有A_Z值0.77±0.04,0.74±0.04和0.80±0.03分别。当根据大多数规则标记肿块时,分别的刺激和外接边缘特征的A_Z值分别为0.92±0.03和0.80±0.03。当仅考虑共识质量时,分别为刺激的A_Z值和外接的边缘特征分别为0.96±0.04和0.87±0.04。我们得出结论,新开发的特征具有高精度,可根据Bi-Rads描述符表征质量利润。

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