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首页> 外文期刊>British Journal of Radiology >Visual grading characteristics (VGC) analysis: a non-parametric rank-invariant statistical method for image quality evaluation.
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Visual grading characteristics (VGC) analysis: a non-parametric rank-invariant statistical method for image quality evaluation.

机译:视觉等级特征(VGC)分析:一种用于图像质量评估的非参数等级不变统计方法。

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摘要

Visual grading of the reproduction of important anatomical structures is often used to determine clinical image quality in radiography. However, many visual grading methods incorrectly use statistical methods that require data belonging to an interval scale. The rating data from the observers in a visual grading study with multiple ratings is ordinal, meaning that non-parametric rank-invariant statistical methods are required. This paper describes such a method for determining the difference in image quality between two modalities called visual grading characteristics (VGC) analysis. In a VGC study, the task of the observer is to rate his confidence about the fulfilment of image quality criteria. The rating data for the two modalities are then analysed in a manner similar to that used in receiver operating characteristics (ROC) analysis. The resulting measure of image quality is the VGC curve, which--for all possible thresholds of the observer for a fulfilled criterion--describes the relationship between the proportions of fulfilled image criteria for the two compared modalities. The area under the VGC curve is proposed as a single measure of the difference in image quality between two compared modalities. It is also described how VGC analysis can be applied to data from an absolute visual grading analysis study.
机译:重要解剖结构的再现的视觉等级通常用于确定放射线照相术中的临床图像质量。但是,许多视觉分级方法错误地使用了统计方法,而统计方法要求数据属于间隔刻度。在具有多个等级的视觉分级研究中,来自观察者的等级数据是有序的,这意味着需要非参数的等级不变统计方法。本文介绍了一种用于确定两种模式之间的图像质量差异的方法,称为视觉渐变特征(VGC)分析。在VGC研究中,观察者的任务是评价他对满足图像质量标准的信心。然后以类似于接收器工作特性(ROC)分析中使用的方式分析这两种模态的额定数据。图像质量的最终度量是VGC曲线,对于观察者对于满足标准的所有可能阈值,VGC曲线描述了两种比较模式的满足图像标准的比例之间的关系。建议将VGC曲线下的面积作为两个比较模态之间图像质量差异的单一度量。还描述了如何将VGC分析应用于来自绝对视觉分级分析研究的数据。

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