Automated Screening for Diabetic Retinopathy by Content*

按内容自动筛查糖尿病视网膜病变*

• 批准号: 7020954
• 负责人: EDWARD CHAUM
• 金额: $ 63.96万
• 依托单位: UNIVERSITY OF TENNESSEE HEALTH SCI CTR
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-30 至 2008-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7020954
• 关键词: automated data processing; bioimaging /biomedical imaging; clinical research; diabetic retinopathy; diagnosis design /evaluation; diagnosis quality /standard; eye disorder diagnosis; glaucoma; human subject; image processing; information retrieval; mathematical model; model design /development; patient oriented research; statistics /biometry

DESCRIPTION (provided by applicant): The application of computer-based imaging to the diagnosis of retinal disease is rapidly becoming a reality. Advances in the imaging of ocular anatomy and pathology can now provide data to diagnose and quantify specific diseases including diabetic retinopathy. The potential for these digital technologies is clear, new computer based systems, and diagnostic algorithms hold the promise of producing low-cost, potentially automated, diagnostic imaging systems. The goal of this project is to investigate the feasibility of a content-based image retrieval (CBIR) method to accurately describe and index human retinal images of diabetic retinopathy collected from low-cost, nondilated retinal photographic examinations. Our goal is to demonstrate the feature-based indexing and retrieval process of CBIR and verify our hypothesis, and novel concept, that retinal pathology can be identified and quantified from visually similar retinal images assembled from a large database comprising images of diabetic retinopathy. The proposed research extends this fundamental investigation by incorporating intrinsic and extrinsic patient data to provide a diagnostic method. We have brought together a unique team that is currently designing the algorithms, developing the analytical tools, and performing the required clinical trials to reach the stated goals of this RFA.

描述（由申请人提供）：基于计算机的成像在视网膜疾病诊断中的应用正在迅速成为现实。眼部解剖学和病理学成像的进展现在可以提供诊断和量化特定疾病（包括糖尿病视网膜病变）的数据。这些数字技术的潜力是显而易见的，新的基于计算机的系统和诊断算法有望生产低成本，潜在的自动化，诊断成像系统。 本项目的目标是研究基于内容的图像检索（CBIR）方法的可行性，以准确地描述和索引从低成本，非散瞳视网膜摄影检查收集的糖尿病视网膜病变的人类视网膜图像。我们的目标是证明基于特征的索引和检索过程的CBIR和验证我们的假设，和新的概念，视网膜病变可以识别和量化从视觉上相似的视网膜图像从一个大型数据库，包括糖尿病视网膜病变的图像。拟议的研究扩展了这一基本调查，结合内在和外在的患者数据，提供一种诊断方法。我们组建了一个独特的团队，目前正在设计算法，开发分析工具，并执行所需的临床试验，以实现该RFA的既定目标。