Visual grading characteristics (VGC) analysis: a non-parametric rank-invariant statistical method for image quality evaluation

人工智能 图像质量 计算机科学 参数统计 计算机视觉 接收机工作特性 数学 模式识别(心理学) 统计 图像(数学) 机器学习
作者
Magnus Båth,L G Månsson
出处
期刊:British Journal of Radiology [Wiley]
卷期号:80 (951): 169-176 被引量:261
标识
DOI:10.1259/bjr/35012658
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.
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