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Computerized scheme for evaluating mammographic phantom images

机译:评估乳房X射线摄影体模图像的计算机化方案

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Purpose: The authors developed a computer algorithm to automatically evaluate images of the American College of Radiology (ACR) mammography accreditation phantom. Methods: The developed algorithm consist of the edge detection of wax insert, nonuniformity correction of background, and correction for magnification and also calculate the cross-correlation coefficient by image matching technique. The algorithm additionally evaluates target shape for fibers, target contrast for speck groups, and target circularity for masses. To obtain an ideal template image without noise and spatial resolution loss, the wax insert containing the embedded test pattern was extracted from the phantom and radiographed. Two template images and ten test phantom images were prepared for this study. The results of evaluation using the algorithm outputs were compared with the averaged results of observer studies by six skilled observers. Results: In comparing the results from the algorithm outputs with the results of observers, the authors found that the computer outputs were well correlated with the evaluations by observers, and they indicate the quality of the phantom image. The correlation coefficients between results of observer studies and two outputs of computer algorithm, i.e., the cross-correlation coefficient by template matching and indices of target shape for fibers, were 0.89 (95% confidence interval, 0.82-0.93; hereinafter the same) and 0.85 (0.76-0.91). The correlation coefficients between observer's results and two outputs: the cross-correlation coefficient and indices of target contrast for speck groups, were 0.83 (0.79-0.86) and 0.85 (0.81-0.88) and between observer's results and two outputs: the cross-correlation coefficient and indices of target circularity for masses, were 0.90 (0.84-0.94) and 0.87 (0.77-0.92). Conclusions: Image evaluation using the ACR phantom is indispensable in quality control of a mammography system. The proposed algorithm is useful for quality control and image evaluation of mammography units.
机译:目的:作者开发了一种计算机算法,可以自动评估美国放射学院(ACR)乳腺X射线摄影认可模型的图像。方法:所开发的算法包括蜡块边缘检测,背景不均匀校正和放大倍率校正,并通过图像匹配技术计算互相关系数。该算法还评估纤维的目标形状,斑点组的目标对比度以及质量的目标圆度。为了获得没有噪声和空间分辨率损失的理想模板图像,从体模中提取包含嵌入测试图案的蜡插入物并进行射线照相。为该研究准备了两个模板图像和十个测试体模图像。将使用算法输出的评估结果与六个熟练观察员的观察员研究的平均结果进行比较。结果:在将算法输出的结果与观察者的结果进行比较时,作者发现计算机输出与观察者的评估有很好的相关性,它们表明了幻像图像的质量。观察者研究结果与计算机算法两个输出之间的相关系数,即模板匹配的互相关系数和纤维的目标形状指数,为0.89(95%置信区间,0.82-0.93;下同)。 0.85(0.76-0.91)。观察者结果与两个输出之间的相关系数:互相关系数和斑点组的目标对比度指数分别为0.83(0.79-0.86)和0.85(0.81-0.88);观察者结果与两个输出之间的相关系数:互相关质量的目标圆度系数和指数分别为0.90(0.84-0.94)和0.87(0.77-0.92)。结论:使用ACR体模进行图像评估在乳腺X线照相系统的质量控制中是必不可少的。所提出的算法对于乳腺X线摄影单元的质量控制和图像评估很有用。

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