IMAGE COMPRESSION OPTIMIZATION FOR CORONARY ANGIOGRAMS

冠状动脉造影图像压缩优化

• 批准号: 2750448
• 负责人: JAMES S WHITING
• 金额: $ 22.33万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-08-01 至 2000-04-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2750448
• 关键词: angiography; computer assisted diagnosis; computer program /software; computer simulation; diagnosis quality /standard; human data; image enhancement; image processing; model design /development; psychophysics

DESCRIPTION (Adapted from Applicant's Abstract): The applicants proposed to develop a computer observer model of the human visual system to be used to evaluate medical image quality more accurately than presently available measures and more quickly and efficiently than human observer performance studies. Such a computer observer model could predict the effect of image compression on performance in clinically relevant tasks and enable automated optimization of image compression algorithms for maximum compression with no degradation in clinical diagnosis of coronary angiograms. To this goal, the applicants propose four specific aims: 1) To perform psychophysical measurements of the effect of image compression on performance in clinically relevant visual tasks (lesion detection and classification) for two popular compression algorithms (JPEG and wavelet) using test images that combine real clinical image noise with simulated lesions; 2) To compare conventional measures of image with current and newly developed computer observer models with respect to their ability to predict the observer task performance measured in Specific Aim 1; 3) To use the computer observer model to optimize the two compression algorithms to achieve maximum compression with minimal performance degradation; and 4) To perform a clinical validation of the optimized compression algorithms using clinical images and physician observers to confirm the predicted minimal degradation of clinical task performance with compression. If successful, the applicants proposed to establish a computer observer model as an accurate and efficient measure of image quality based on clinical task performance, which will be a valuable tool for developing optimal image processing and compression algorithms. The impact of this research will be more rapid and cost effective communication and storage of digital coronary angiograms without loss of diagnostic information.

描述（改编自申请人的摘要）：申请人建议 开发人类视觉系统的计算机观察者模型，用于 比目前更准确地评估医学图像质量 测量并且比人类观察者的表现更快更有效 研究。 这样的计算机观察者模型可以预测图像的效果 压缩临床相关任务的性能并实现自动化 优化图像压缩算法，实现最大压缩，无需 冠状动脉造影临床诊断的退化。 为了这个目标， 申请人提出了四个具体目标： 1) 进行心理物理学 图像压缩对临床性能影响的测量 两种流行的相关视觉任务（病变检测和分类） 使用测试图像的压缩算法（JPEG 和小波） 具有模拟病变的真实临床图像噪声； 2）与传统的比较 使用当前和新开发的计算机观察模型进行图像测量 关于他们预测观察者任务绩效的能力 在具体目标 1 中测量； 3）利用计算机观察模型 优化两种压缩算法以实现最大压缩 性能下降最小； 4) 进行临床验证 使用临床图像和医生的优化压缩算法 观察员确认临床任务的预测最小退化 压缩性能。 如果成功，申请人建议 建立计算机观察者模型作为准确有效的测量 基于临床任务表现的图像质量，这将是一个有价值的 用于开发最佳图像处理和压缩算法的工具。 这项研究的影响将更加迅速且更具成本效益 数字冠状动脉造影的通信和存储不会丢失 诊断信息。