Abbey C K, Barrett H H
Department of Radiology, University of Arizona, Tucson 85724, USA.
J Opt Soc Am A Opt Image Sci Vis. 2001 Mar;18(3):473-88. doi: 10.1364/josaa.18.000473.
We consider detection of a nodule signal profile in noisy images meant to roughly simulate the statistical properties of tomographic image reconstructions in nuclear medicine. The images have two sources of variability arising from quantum noise from the imaging process and anatomical variability in the ensemble of objects being imaged. Both of these sources of variability are simulated by a stationary Gaussian random process. Sample images from this process are generated by filtering white-noise images. Human-observer performance in several signal-known-exactly detection tasks is evaluated through psychophysical studies by using the two-alternative forced-choice method. The tasks considered investigate parameters of the images that influence both the signal profile and pixel-to-pixel correlations in the images. The effect of low-pass filtering is investigated as an approximation to regularization implemented by image-reconstruction algorithms. The relative magnitudes of the quantum and the anatomical variability are investigated as an approximation to the effects of exposure time. Finally, we study the effect of the anatomical correlations in the form of an anatomical slope as an approximation to the effects of different tissue types. Human-observer performance is compared with the performance of a number of model observers computed directly from the ensemble statistics of the images used in the experiments for the purpose of finding predictive models. The model observers investigated include a number of nonprewhitening observers, the Hotelling observer (which is equivalent to the ideal observer for these studies), and six implementations of channelized-Hotelling observers. The human observers demonstrate large effects across the experimental parameters investigated. In the regularization study, performance exhibits a mild peak at intermediate levels of regularization before degrading at higher levels. The exposure-time study shows that human observers are able to detect ever more subtle lesions at increased exposure times. The anatomical slope study shows that human-observer performance degrades as anatomical variability extends into higher spatial frequencies. Of the observers tested, the channelized-Hotelling observers best capture the features of the human data.
我们考虑在有噪声的图像中检测结节信号轮廓，这些图像旨在大致模拟核医学断层图像重建的统计特性。图像有两个变异性来源，一个是成像过程中的量子噪声，另一个是被成像物体集合中的解剖学变异性。这两个变异性来源均由平稳高斯随机过程模拟。通过对白噪声图像进行滤波来生成此过程的样本图像。通过使用二选一强制选择法进行心理物理学研究，评估人类观察者在多个信号精确已知检测任务中的表现。所考虑的任务研究影响图像信号轮廓和像素间相关性的图像参数。研究低通滤波的效果，将其作为图像重建算法实现的正则化的近似。研究量子和解剖学变异性的相对大小，将其作为曝光时间影响的近似。最后，我们研究解剖学斜率形式的解剖学相关性的影响，将其作为不同组织类型影响的近似。为了找到预测模型，将人类观察者的表现与直接根据实验中使用的图像的总体统计计算出的多个模型观察者的表现进行比较。所研究的模型观察者包括一些非白化观察者、霍特林观察者（在这些研究中相当于理想观察者）以及六种通道化霍特林观察者的实现。人类观察者在所研究的实验参数上表现出很大的影响。在正则化研究中，性能在正则化的中间水平处呈现出一个温和的峰值，然后在更高水平时下降。曝光时间研究表明，随着曝光时间增加，人类观察者能够检测到越来越细微的病变。解剖学斜率研究表明，随着解剖学变异性扩展到更高空间频率，人类观察者的表现会下降。在所测试的观察者中，通道化霍特林观察者最能捕捉人类数据的特征。
