MEASUREMENT OF DIFFERENTIAL IMAGE QUALITY

描述(改编自申请者摘要)：本报告的目的 研究的目的是开发一种确定诊断差异的方法 任意两个模式A和B之间的任务绩效。新技术是 称为配对图像(1)观察者绩效方法。虽然这项研究 将使用x射线图像，预计1方法将具有更广泛的应用范围 适用性(例如，CT与MRI)。在1方法中，向读取器显示一个 同一受试者的一对图像，来自每种模式。观察者 选择对指定诊断任务更好的图像，并 指定评级(A&>；&B、A&>B、A=B、A&lt；B、A&lt；&lt；B)。对本实验的分析 生成差分ROC曲线(！ROC)下的面积。开发后 该方法将应用于人体体模和临床图像。 加工工艺的多样性。临床任务是检测 微钙化、肿块检测、分类 微钙化和肿块分类。申请者建议 要将该方法扩展到其他观察者性能实验，包括 本地化信息，即FROC和LROC，并评估可行性 将其应用于计算机辅助设计评价。在每种情况下，他们都会测量 1方法的统计能力比传统ROC方法的改进 方法。申请者建议将该方法应用于评估 几种压缩算法在数字化乳腺摄影中的应用效果。第一个 方法有可能检测到图像中非常细微的差异 质量，比目前的ROC方法可检测的小得多。会的 允许快速优化成像系统，而无需昂贵的 而且往往包括中华民国的研究。压缩算法的评估结果如下 预计将对需要设计的成像科学家和工程师有用 更具成本效益的数字乳房X光检查系统。这将极大地 促进在乳房X光照相中采用数字方法，从而引导 为改善乳腺癌妇女的医疗保健服务。

DESCRIPTION (Adapted from Applicant's Abstract): The objective of this research is to develop a method for determining differences in diagnostic task performance between any two modalities A and B. The new technique is called the paired image (1) observer performance method. While the research will be done with x-ray images, the 1 method is expected to have much wider applicability (e.g., CT verses MRI). In the 1 method, the reader is shown a pair of images of the same subject, from each modality. The observer selects the image that is superior for the specified diagnostic task and assigns a rating (A>>B,A>B,A=B,A<B,A<<B). Analysis of this experiment yields the area under the Differential ROC curve (!ROC). After development of the method, it will be applied to phantom and clinical images under a variety of processings. The clinical tasks are detection of microcalcifications, detection of masses, classification of microcalcifications,and classification of masses. The applicants proposed to extend the method to other observer performance experiments that include localization information, namely, FROC and LROC,and assess the feasibility of applying it to CAD evaluation. In each case, they would measure the improvement in statistical power of the 1 method over the traditional ROC method. The applicants proposed to apply the method to evaluating the effect of several compression algorithms in digital mammography. The 1 method has the potential for detecting very subtle differences in image quality, much smaller than is detectable by the present ROC method. It will allow rapid optimization of imaging systems, without the need for expensive and often inclusive ROC studies. The compression algorithm evaluation is expected to be useful to imaging scientists and engineers who need to design more cost-effective digital mammography systems. This will greatly facilitate the adoption of digital methods in mammography and thereby lead to improved health care for women with breast cancer.