Brooks K W, Trueblood J H, Kearfott K J, Lawton D T
Department of Radiation Oncology, Emory Clinic, Atlanta, Georgia 30322, USA.
Med Phys. 1997 May;24(5):709-23. doi: 10.1118/1.597992.
A significant metric in federal mammography quality standards is the phantom image quality assessment. The present work seeks to demonstrate that automated image analyses for American College of Radiology (ACR) mammographic accreditation phantom (MAP) images may be performed by a computer with objectivity, once a human acceptance level has been established. Twelve MAP images were generated with different x-ray techniques and digitized. Nineteen medical physicists in diagnostic roles (five of which were specially trained in mammography) viewed the original film images under similar conditions and provided individual scores for each test object (fibrils, microcalcifications, and nodules). Fourier domain template matching, used for low-level processing, combined with derivative filters, for intermediate-level processing, provided translation and rotation-independent localization of the test objects in the MAP images. The visibility classification decision was modeled by a Bayesian classifer using threshold contrast. The 50% visibility contrast threshold established by the trained observers' responses were: fibrils 1.010, microcalcifications 1.156, and nodules 1.016. Using these values as an estimate of human observer performance and given the automated localization of test objects, six images were graded with the computer algorithm. In all but one instance, the algorithm scored the images the same as the diagnostic physicists. In the case where it did not, the margin of disagreement was 10% due to the fact that the human scoring did not allow for half-visible fibrils (agreement occurred for the other test objects). The implication from this is that an operator-independent, machine-based scoring of MAP images is feasible and could be used as a tool to help eliminate the effect of observer variability within the current system, given proper, consistent digitization is performed.
联邦乳腺钼靶质量标准中的一项重要指标是体模图像质量评估。本研究旨在证明，一旦确定了人类可接受水平，计算机就可以客观地对美国放射学会（ACR）乳腺钼靶认证体模（MAP）图像进行自动图像分析。使用不同的X射线技术生成了12幅MAP图像并进行数字化处理。19名担任诊断角色的医学物理学家（其中5名接受过乳腺钼靶专门培训）在相似条件下查看原始胶片图像，并为每个测试对象（纤维、微钙化和结节）给出个人评分。用于低级处理的傅里叶域模板匹配与用于中级处理的导数滤波器相结合，可在MAP图像中提供与平移和旋转无关的测试对象定位。可见性分类决策由使用阈值对比度的贝叶斯分类器建模。训练有素的观察者的反应确定的50%可见性对比度阈值为：纤维1.010、微钙化1.156和结节1.016。将这些值用作人类观察者性能的估计，并考虑到测试对象的自动定位，使用计算机算法对6幅图像进行了评分。除了一个实例外，在所有情况下，算法对图像的评分与诊断物理学家相同。在不同的那个实例中，分歧幅度为10%，原因是人工评分不考虑半可见纤维（对其他测试对象的评分一致）。由此得出的结论是，在进行适当、一致的数字化处理的情况下，对MAP图像进行独立于操作员的基于机器的评分是可行的，并且可以用作一种工具来帮助消除当前系统中观察者变异性的影响。
