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Diagnostic imaging - evaluating image quality using visual grading characteristic (VGC) analysis

机译:诊断成像 - 使用视觉分级特性评估图像质量(VGC)分析

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摘要

Radiologists are regularly faced with the task of comparing image quality obtained using different imaging systems or settings. Visual grading techniques can be used to evaluate the quality of images by grading the clarity of reproduction of anatomical or pathological structures. The methods, which include "visual grading analysis (VGA)" and the "image criteria (IC) study", are characterised by their attractive simplicity and reliability. Non-parametric rank-invariant statistical methods are suitable techniques for statistical analysis of VGA-data. BAyenth and MAyennsson (2007) introduced such a method and termed it "visual grading characteristics (VGC) analysis". This paper gives an overview of the principle together with an example of its use in veterinary radiology. The aim of this review article is to encourage veterinary researchers to apply this method which has proven valuable in the human field. Basically, the method can also be applied for the analysis of other categories of images (e.g. histological sections, cytological smears) in cases where the task is to evaluate features subjectively on the basis of a score, allowing some degree of freedom of decision. Furthermore, the aim of the investigation is not necessarily restricted to quality aspects. Other questions such as the effects of treatment options on the appearance of certain structures can be compared as well.
机译:放射科医师定期面临比较使用不同的成像系统或设置获得的图像质量的任务。可视化分级技术可用于通过分级对解剖结构或病理结构的再现的清晰度来评估图像的质量。包括“视觉分析分析(VGA)”和“图像标准(IC)研究”的方法,其特征在于它们具有吸引力和可靠性。非参数秩级别统计方法是用于VGA数据的统计分析的合适技术。 Bayenth和Mayennsson(2007)介绍了这种方法并称之为“视觉分级特性(VGC)分析”。本文概述了本原理的概述,以及其在兽医放射学中的示例。本综述文章的目的是鼓励兽医研究人员应用这种方法,该方法已经证明了在人类领域有价值。基本上,该方法也可以应用于分析其他类别的图像(例如组织学部分,细胞学涂片),在任务是基于分数的主观评估特征的情况下,允许一定程度的决定自由。此外,调查的目的不一定限于质量方面。还可以比较其他问题,例如治疗方面对某些结构外观的影响。

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