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

数码眼底相机的实时、自动图像质量评估

• 批准号: 7481666
• 负责人: Peter none Soliz
• 金额: $ 9.91万
• 依托单位: VISIONQUEST BIOMEDICAL
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7481666
• 关键词: Address; Algorithms; Applications Grants; Back; Characteristics; Classification; Color; Computers; Data; Data Set; Databases; Doctor of Philosophy; Effectiveness; Exposure to; Feedback; Film; Fundus; Goals; Human; Image; Image Analysis; Individual; Iowa; Lead; Least-Squares Analysis; Left; Link; Manufacturer Name; Medicine; Methodology; Methods; Metric; Modeling; Movement; Ophthalmologist; Ophthalmology; Patients; Perception; Pilot Projects; Principal Investigator; Process; Radiation; Radiology Specialty; Receiver Operating Characteristics; Reporting; Research; Research Personnel; Retinal; Score; Screening procedure; Sensitivity and Specificity; Source; Specialist; Specificity; Standards of Weights and Measures; Study Section; System; Techniques; Testing; Time; Training; Universities; Validation; Variant; Vision; base; digital; digital imaging; evaluation/testing; experience; feeding; image processing; indexing; interest; research and development; success; vector

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.

描述（由申请人提供）：眼科从35 mm彩色胶片到数字媒体的快速过渡提供了在拍摄照片后立即评估单个数字图像质量的机会。在其他医学领域，如放射学，图像质量已经得到解决，以减少不必要的辐射暴露。该项目的目标是展示一种方法，该方法将实时评估眼底照相机的数字图像，并向操作员反馈图像质量以及质量差图像中问题的可能来源。具体目标是改进和应用一种方法，该方法在使用训练集（N = 200）分级图像检测质量差或不可接受的图像时计算效率高且非常有效，并在一个大的（N = 800）视网膜图像集上对其进行测试。该方法是基于已成功测试的初步数据集，并在视觉和眼科研究协会（ARVO）2007年报告的技术。所提出的方法的成功演示将导致成为患者记录的一部分的低质量视网膜图像的显著减少和/或对涉及视网膜图像的研究的负面影响的减少。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。由于该方法使用由人类感知质量建议的参数，因此图像质量模型将产生与分级者的结果相当的结果。该方法将基于分级员或眼科医生分配的图像质量评分。商业上，实时图像质量评估系统是感兴趣的眼底相机和我们的方法的许多制造商将被证明是可扩展的任何数字成像仪。我们的方法也将是非常有价值的筛选中心，质量差的图像可以立即报告给当地或远程摄影师。