计算机化的乳腺摄影认证体模图像定量评估。
Computerized quantitative evaluation of mammographic accreditation phantom images.
机构信息
Department of Radiological Technology, School of Health Sciences, Niigata University, 2-746 Asahimachidori, Chuouku, Niigata 951-8518, Japan.
出版信息
Med Phys. 2010 Dec;37(12):6323-31. doi: 10.1118/1.3516238.
PURPOSE
The objective was to develop and investigate an automated scoring scheme of the American College of Radiology (ACR) mammographic accreditation phantom (RMI 156, Middleton, WI) images.
METHODS
The developed method consisted of background subtraction, determination of region of interest, classification of fiber and mass objects by Mahalanobis distance, detection of specks by template matching, and rule-based scoring. Fifty-one phantom images were collected from 51 facilities for this study (one facility provided one image). A medical physicist and two radiologic technologists also scored the images. The human and computerized scores were compared.
RESULTS
In terms of meeting the ACR's criteria, the accuracies of the developed method for computerized evaluation of fiber, mass, and speck were 90%, 80%, and 98%, respectively. Contingency table analysis revealed significant association between observer and computer scores for microcalcifications (p<5%) but not for masses and fibers.
CONCLUSIONS
The developed method may achieve a stable assessment of visibility for test objects in mammographic accreditation phantom image in whether the phantom image meets the ACR's criteria in the evaluation test, although there is room left for improvement in the approach for fiber and mass objects.
目的
旨在开发并研究一种自动评分方案，用于评估美国放射学院（ACR）乳腺影像认证模体（RMI 156， Middleton，WI）的图像。
方法
该方法包括背景减除、感兴趣区域的确定、基于马氏距离的纤维和肿块分类、模板匹配的斑点检测以及基于规则的评分。本研究共采集了 51 个来自 51 个医疗机构的模体图像（一个医疗机构提供一个图像）。一名医学物理学家和两名放射技师也对这些图像进行了评分。比较了人工和计算机评分。
结果
根据 ACR 的标准，该方法对纤维、肿块和斑点的计算机评估的准确率分别为 90%、80%和 98%。列联表分析表明，观察者和计算机在微钙化方面的评分具有显著相关性（p<5%），但在肿块和纤维方面则没有。
结论
该方法可实现对乳腺影像认证模体图像中测试对象可视性的稳定评估，无论该模体图像在评估测试中是否符合 ACR 的标准，尽管在纤维和肿块对象的方法上还有改进的空间。
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