Real-time, Automatic Image Quality Assessment for Digital Fundus Cameras

描述(申请人提供)：眼科从35 mm彩色胶片到数字媒体的快速过渡提供了在照片拍摄后立即评估个人数字图像质量的机会。在其他医学领域，例如放射学，已经解决了图像质量问题，以减少不必要的辐射暴露。这个项目的目标是展示一种方法，该方法将实时评估来自眼底相机的数字图像，并向操作员反馈图像质量和质量较差图像中可能存在的问题来源。具体目标是改进和应用一种在使用训练集(N=200)分级图像来检测质量较差或不可接受的图像方面既计算高效又非常有效的方法，并在大的(N=800)组视网膜图像上进行测试。该方法是基于已经在初步数据集上成功测试并在2007年视觉和眼科研究协会(ARVO)上报告的技术。建议的方法的成功演示将显著减少成为患者记录一部分的劣质视网膜图像，和/或减少对涉及视网膜图像的研究的负面影响。通过向摄影师提供实时反馈，可以采取纠正措施，消除数据丢失或给患者带来的不便。由于该方法使用的参数是由人的感知质量所建议的，因此图像质量模型将产生与评分者相当的结果。该方法将基于评分员或眼科医生分配的图像质量分数。在商业上，许多眼底相机制造商对实时图像质量评估系统感兴趣，我们的方法将被证明可扩展到任何数字成像器。我们的方法对筛选中心也很有价值，在那里，质量不佳的图像可以立即报告给本地或远程摄影师。

DESCRIPTION (provided by applicant): The rapid transition in ophthalmology from 35mm color film to digital media provides an opportunity to evaluate individual digital images for quality immediately after the photograph is taken. In other fields of medicine, such as radiology, image quality has been addressed in order to reduce unnecessary exposure to radiation. The goal of this project is to demonstrate a methodology that will evaluate a digital image from a fundus camera in real-time and give the operator feedback as to the quality of the image and the possible source of the problem in poor quality images. The specific aims are to refine and apply a methodology that is both computationally efficient and highly effective in detecting poor or unacceptable quality images using a training set (N = 200) graded images, and to test it on a large (N = 800) set of retinal images. The approach is based on techniques that have been tested successfully on a preliminary data set and reported at the Association for Research in Vision and Ophthalmology (ARVO) 2007. The successful demonstration of the proposed methodology will lead to a significant reduction in poor quality retinal images that become part of a patient's record and/or a reduction in the negative impact on studies involving retinal images. By providing real-time feedback to the photographer, corrective actions can be taken and loss of data or inconvenience to the patient eliminated. Because the methodology uses parameters that are suggested by human perception qualities, the image quality model will produce results comparable to those of graders. The methodology will be based on image quality scores assigned by graders or ophthalmologists. Commercially, a real-time image quality assessment system is of interest to many manufacturers of fundus cameras and our methodology will be demonstrated to be scalable to any digital imager. Our methodology will also be of great value to screening centers where poor quality images can be reported immediately to the local or remote photographer.