Zhang Yani, Pham Binh T, Eckstein Miguel P
Department of Psychology, University of California, Santa Barbara, USA.
Acad Radiol. 2005 Mar;12(3):324-36. doi: 10.1016/j.acra.2004.09.015.
The set partitioning in hierarchical trees (SPIHT) wavelet image compression algorithm with the human visual system (HVS) quantization matrix was investigated using x-ray coronary angiograms. We tested whether the HVS quantization matrix for the SPIHT wavelet compression improved computer model/human observer performance in a detection task with variable signals compared to performance with the default quantization matrix. We also tested the hypothesis of whether evaluating the rank order of the two quantization matrices (HVS versus default) based on performance of computer model observers in a signal known exactly but variable task (SKEV) generalized to model/human performance in the more clinically realistic signal known statistically task (SKS).
Nine hundred test images were created using real x-ray coronary angiograms as backgrounds and simulated arteries with filling defects (signals). The task for the model and human observer was to detect which one of the four computer simulated arterial segments contained the signal, four alternative-forced-choice (4 AFC). We obtained performance for four model observers (nonprewhitening matched filter with an eye filter, Hotelling, Channelized Hotelling, and Laguerre Gauss Hotelling model observers) for both the SKEV and SKS tasks with images compressed with and without the HVS quantization matrix. A psychophysical study measured performance from three human observers for the same conditions and tasks as the model observers.
Performance for all four model observers improved with the use of the HVS quantization scheme. Improvements ranged from 5% (at compression ratio 7:1) to 50% (at compression ratio 30:1) for both the SKEV and SKS tasks. Human observer performance improvement averaged across observers ranged from 6% (at compression ratio 7:1) to 35% (at compression ratio 30:1) for the SKEV task and from 2% (at compression ratio 7:1) to 38% (at compression ratio 30:1) for the SKS task. Addition of internal noise to the model observers allowed for good prediction of human performance.
Use of the HVS quantization scheme in the SPIHT wavelet compression led to improved model and human observer performance in clinically relevant detection tasks in x-ray coronary angiograms. Model observer performance can be reliably used to predict the human observer performance for the studied tasks as a function of SPIHT wavelet image compression. Our results further confirmed that model observer performance in the computationally more tractable SKEV task can be potentially used as a figure of merit for the more clinically realistic SKS task with real anatomic backgrounds.
使用X射线冠状动脉造影研究了带有人类视觉系统（HVS）量化矩阵的分层树状集合划分（SPIHT）小波图像压缩算法。我们测试了与默认量化矩阵相比，SPIHT小波压缩的HVS量化矩阵在可变信号检测任务中是否能提高计算机模型/人类观察者的性能。我们还测试了基于计算机模型观察者在精确已知但可变信号任务（SKEV）中的性能评估两种量化矩阵（HVS与默认矩阵）的排序是否能推广到更具临床现实意义的统计已知信号任务（SKS）中的模型/人类性能。
使用真实的X射线冠状动脉造影作为背景并模拟带有充盈缺损（信号）的动脉创建了900张测试图像。模型和人类观察者的任务是检测四个计算机模拟动脉段中的哪一个包含信号，即四择一迫选（4AFC）。我们获得了四个模型观察者（带眼滤波器的非白化匹配滤波器、霍特林、通道化霍特林和拉盖尔高斯霍特林模型观察者）在使用和不使用HVS量化矩阵压缩的图像的SKEV和SKS任务中的性能。一项心理物理学研究测量了三名人类观察者在与模型观察者相同条件和任务下的性能。
使用HVS量化方案后，所有四个模型观察者的性能均有所提高。对于SKEV和SKS任务，性能提升范围从5%（压缩比为7:1时）到50%（压缩比为30:1时）。SKEV任务中，观察者平均性能提升范围从6%（压缩比为7:1时）到35%（压缩比为30:1时）；SKS任务中，从2%（压缩比为7:1时）到38%（压缩比为30:1时）。给模型观察者添加内部噪声可以很好地预测人类性能。
在SPIHT小波压缩中使用HVS量化方案可提高X射线冠状动脉造影临床相关检测任务中模型和人类观察者的性能。模型观察者的性能可以可靠地用于预测所研究任务中人类观察者的性能，作为SPIHT小波图像压缩的函数。我们的结果进一步证实，在计算上更易处理的SKEV任务中模型观察者的性能有可能用作具有真实解剖背景的更具临床现实意义的SKS任务的品质因数。
