MODEL OBSERVERS FOR COMPRESSION OF CORONARY ANGIOGRAMS

基于观察者研究的评估是耗时和昂贵的，优化是不可行的。因此，具有观察者研究的预测准确性但具有时间和成本效益的图像质量测量有很强的理由。我们建立在我们以前的计算机模型观测器的工作，扩展模型观测器的应用从基本的检测任务，病变是不变的（检测/信号-确切地知道D-SKE任务），以更现实的任务，出现在图像中的病变可以是不同的类型和大小（检测-分类/信号已知统计或DC-SKS任务）。我们的目标是开发一个计算机模型观测器，可用于预测这些更现实的检测/分类任务与病变变异性的图像压缩的效果。该模型可用于优化压缩算法参数方面的任务性能，在这些更现实的任务。为了实现这一目标，我们提出了五个具体目标：1）开发计算机模型观察员的任务，观察员必须检测和分类病变，病变有不同的大小和/或方向。2)在DC-SKS任务中对五种不同的最先进的图像压缩算法（其中一些被认为是JPEG 2000国际标准）进行心理物理测量，并将其与我们以前的D-SKE任务结果进行比较。3)比较新提出的DC-SKS模型观测器预测观测器任务性能的能力，具体目标2。4)使用具有最高预测能力的DC-SKS模型观测器，使用模拟退火技术对压缩算法进行自动优化。5)使用默认压缩参数、DCS-SKS模型优化参数和更传统的D-SKE模型优化参数，对压缩算法对任务的影响进行心理物理学比较。如果成功的话，我们将把模型观测器的使用扩展到更现实的任务中。本研究的影响将是改进基于计算机的指标，用于医学图像质量的成本和时间效率评估，以及更快速和更具成本效益的数字冠状动脉血管造影片的通信和存储。

Evaluation based on observer studies is time consuming and costly and optimization is just not viable. There is therefore a strong rationale for image quality measures with the predictive accuracy of observer studies but that are time and cost efficient. We build on our previous work on computer model observers to extend the application of model observers from basic detection tasks where the lesion is invariant (detection/signal- known exactly on D-SKE tasks) to more realist tasks where the lesion appearing in the image can be of different types and sizes (detection- classification/signal known statistically or DC-SKS tasks). Our goal is to develop a computer model observer that can be used to predict the effect of image compression in these more realistic detection/classification tasks with lesion variability. This model could be used for optimization of compression algorithm parameters with respect to task performance in these more realistic tasks. To achieve this goal we propose five specific aims: 1) To develop computer model observers for tasks where the observers have to detect and classify a lesion and where the lesions have different sizes and/or orientations. 2) To perform psychophysical measurements of five different state of the art image compression algorithms (some of them which are being considered as the JPEG 2000 international standard) in the DC-SKS task and compare it to our previous results with D-SKE tasks. 3) To compare the newly proposed DC-SKS model observers with respect to their ability to predict the observer task performance measured in specific aim 2. 4) To use the DC-SKS model observer with highest predictive power to perform automated optimization of compression algorithms using stimulated annealing techniques. 5) To perform psychophysical comparisons of the effects of the compression algorithms on tasks with the default compression parameters, DCS-SKS model optimized parameters, and the more traditional D-SKE model optimized parameters. If successful, we will extend the use of model observers to more realistic tasks. The impact of this research will be improved computer based metrics for cost and time efficient evaluation of medical image quality as well as more rapid and cost effective communication and storage of digital coronary angiograms.