斜坡谱噪声下定位性能的分类图像。
Classification images for localization performance in ramp-spectrum noise.
机构信息
Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, 93106, USA.
Division of Imaging Diagnostics and Software Reliability, United States Food and Drug Administration, White Oaks, MD, 20993, USA.
出版信息
Med Phys. 2018 May;45(5):1970-1984. doi: 10.1002/mp.12857. Epub 2018 Apr 11.
PURPOSE
This study investigates forced localization of targets in simulated images with statistical properties similar to trans-axial sections of x-ray computed tomography (CT) volumes. A total of 24 imaging conditions are considered, comprising two target sizes, three levels of background variability, and four levels of frequency apodization. The goal of the study is to better understand how human observers perform forced-localization tasks in images with CT-like statistical properties.
METHODS
The transfer properties of CT systems are modeled by a shift-invariant transfer function in addition to apodization filters that modulate high spatial frequencies. The images contain noise that is the combination of a ramp-spectrum component, simulating the effect of acquisition noise in CT, and a power-law component, simulating the effect of normal anatomy in the background, which are modulated by the apodization filter as well. Observer performance is characterized using two psychophysical techniques: efficiency analysis and classification image analysis. Observer efficiency quantifies how much diagnostic information is being used by observers to perform a task, and classification images show how that information is being accessed in the form of a perceptual filter.
RESULTS
Psychophysical studies from five subjects form the basis of the results. Observer efficiency ranges from 29% to 77% across the different conditions. The lowest efficiency is observed in conditions with uniform backgrounds, where significant effects of apodization are found. The classification images, estimated using smoothing windows, suggest that human observers use center-surround filters to perform the task, and these are subjected to a number of subsequent analyses. When implemented as a scanning linear filter, the classification images appear to capture most of the observer variability in efficiency (r = 0.86). The frequency spectra of the classification images show that frequency weights generally appear bandpass in nature, with peak frequency and bandwidth that vary with statistical properties of the images.
CONCLUSIONS
In these experiments, the classification images appear to capture important features of human-observer performance. Frequency apodization only appears to have a significant effect on performance in the absence of anatomical variability, where the observers appear to underweight low spatial frequencies that have relatively little noise. Frequency weights derived from the classification images generally have a bandpass structure, with adaptation to different conditions seen in the peak frequency and bandwidth. The classification image spectra show relatively modest changes in response to different levels of apodization, with some evidence that observers are attempting to rebalance the apodized spectrum presented to them.
目的
本研究旨在模拟 X 射线计算机断层扫描（CT）容积的横断面，对具有统计学特征的目标进行强制定位。共考虑了 24 种成像条件，包括两种目标大小、三种背景变化水平和四种频率调制度。该研究的目的是更好地理解人类观察者在具有 CT 统计特性的图像中执行强制定位任务的方式。
方法
采用平移不变传递函数对 CT 系统的传递特性进行建模，同时采用调制度滤波器对高频进行调制。图像中包含噪声，由 ramp-spectrum 分量（模拟 CT 采集噪声的影响）和幂律分量（模拟背景中的正常解剖结构的影响）组成，这两个分量都由调制度滤波器调制。使用两种心理物理学技术来描述观察者的性能：效率分析和分类图像分析。观察者效率量化了观察者执行任务时使用了多少诊断信息，而分类图像则以感知滤波器的形式显示了信息是如何被访问的。
结果
五项研究的心理物理学研究构成了结果的基础。不同条件下观察者效率的范围从 29%到 77%。在均匀背景条件下，观察效率最低，并且发现了调制度的显著影响。使用平滑窗口估计的分类图像表明，人类观察者使用中心环绕滤波器来执行任务，并且对这些滤波器进行了多项后续分析。当作为扫描线性滤波器实现时，分类图像似乎可以捕获观察者效率的大部分可变性（r = 0.86）。分类图像的频率谱表明，频率权重通常呈带通性质，其峰值频率和带宽随图像的统计特性而变化。
结论
在这些实验中，分类图像似乎可以捕获人类观察者性能的重要特征。只有在没有解剖变异性的情况下，调制度才会对性能产生显著影响，在这种情况下，观察者似乎低估了相对噪声较小的低空间频率。从分类图像得出的频率权重通常具有带通结构，在不同条件下会出现峰值频率和带宽的适应。分类图像频谱对不同的调制度变化响应相对较小，有一些证据表明观察者试图重新平衡呈现给他们的调制度频谱。
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