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首页> 外文会议>Image Perception, Observer Performance, and Technology Assessment >Metrics of medical image quality: task-based model observers vs. image discrimination/perceptual difference models
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Metrics of medical image quality: task-based model observers vs. image discrimination/perceptual difference models

机译：医学图像质量度量：基于任务的模型观察者与图像辨别/感知差异模型

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There have been two distinct approaches to develop human vision models that can be used to perform automated evaluation and optimization of medical image quality: linear task based model observers vs. perceptual difference/image discrimination models. Although these two approaches are very different there has been little work directly comparing them in their ability to optimize human performance in clinically relevant tasks. We compared the effectiveness of these two types of metrics of image quality to perform automated computer optimization of JPEG 2000 image compression encoder settings using test images that combined real x-ray coronary angiogram backgrounds with simulated filling defects of 184 different size/shapes. A genetic algorithm was used to optimize the JPEG 2000 encoder settings with respect to: a) a particular task based model observer performance (non-prewhitening matched filter with an eye filter, NPWE; b) a particular perceptual difference/image discrimination model error metric (DCTune2.0; NASA Ames Research Center). A subsequent human psychophysical study was conducted to evaluate the effect of the two different optimized compression encoder settings on visual detection of the simulated filling defect in one of four locations (four alternative forced choice; 4 AFC). Results show that optimizing JPEG 2000 encoder settings with respect to both the NPWE performance and DCTune 2.0 perceptual error lead to improved human task performance relative to human performance with the default encoder settings. However, the NPWE-optimization led to much greater human performance improvement than the perceptual difference model optimization.
机译：有两种不同的方法可以开发可用于执行自动评估和医学图像质量优化的人类视觉模型：基于线性任务的模型观察者与感知差异/图像辨别模型。尽管这两种方法截然不同，但在将它们在临床相关任务中优化人类绩效的能力上进行直接比较的工作很少。我们比较了这两种类型的图像质量指标的有效性，以使用结合了真实X射线冠状动脉血管造影背景和184种不同大小/形状的模拟填充缺陷的测试图像对JPEG 2000图像压缩编码器设置进行自动计算机优化。使用遗传算法来针对以下方面优化JPEG 2000编码器设置：a）基于特定任务的模型观察者性能（带有眼图滤镜的非预增白匹配滤镜，NPWE; b）特定的感知差异/图像辨别模型误差度量（DCTune2.0; NASA艾姆斯研究中心）。随后进行了人类心理物理研究，以评估两种不同的优化压缩编码器设置对在四个位置之一（四个替代性强制选择; 4个AFC）中的模拟填充缺陷进行视觉检测时的效果。结果表明，相对于NPWE性能和DCTune 2.0感知错误，优化JPEG 2000编码器设置相对于默认编码器设置下的人类性能而言，可提高人工任务性能。但是，与感知差异模型优化相比，NPWE优化带来了更大的人类绩效提升。

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