Retinal Vessel Measurement and Characterization System

描述（申请人提供）：视网膜微血管异常与许多眼部和全身性疾病有关，包括糖尿病视网膜病变、高血压、中风和脑血管疾病。例如，全身性小动脉狭窄和动静脉划痕似乎是高血压的不可逆转的长期标志。研究结果表明，视网膜摄影可能有助于评估风险分层和筛查视网膜疾病的适当人群。高度发展的成像解决方案和计算机处理能力为量化这些异常打开了一扇门。由于计算机自动化，建议一种有效和全面的方法，这种方法是可重复的，没有阅读器之间和阅读器内部的可变性，精确，并且与人类分析人员的技术一致。我们建议开发和验证一个系统，提取视网膜血管，分类他们的分叉和交叉，并量化视网膜血管培养系统的直径测量。Auto-Cal系统将使用先进的形态学和高斯滤波技术来分割血管网络。分割后的交叉和分叉将通过形态骨架化、路径遍历和修剪算法进行分类。然后通过将灰度轮廓拟合到描述典型血管的高斯参数来计算血管直径。该系统将在50张数字图像上进行测试，这些图像将使用先前开发的计算机辅助工具手动测量8条主要血管。与人工导出的血管直径的高统计相关性将决定该过程的成功。

DESCRIPTION (provided by applicant): Retinal microvascular abnormalities are related to many ocular and systemic diseases including diabetic retinopathy, hypertension, stroke, and cerebral micro-vascular diseases. For example generalized arteriolar narrowing and arteriovenous nicking appear to be irreversible long-term markers of hypertension. Findings suggest that retinal photography may be useful for assessing risk stratification and screening for retinal disease in appropriate populations. Highly evolved imaging solutions and computer processing power have opened a door to quantify these abnormalities. A methodology is suggested that is efficient and comprehensive because of computer automation, that is repeatable, free of inter- and intra-reader variability, precise, and consistent with the human analysts' techniques. We propose to develop and validate a system that extracts retinal vessels, classifies their bifurcations and crossings, and quantifies a diameter measurement of the retinal vas-culature system. The Auto-Cal system will use advanced morphological and Gaussian filtering techniques to segment the vessel network. Once segmented the crossings and bifurcations will be classified through morphological skeletonization path traversing and pruning algorithms. The vessel diameter is then calculated by fitting the gray level profile to Gaussian parameters that describe a typical vessel. The system will be tested on 50 digital images, which will have their 8 major vessels manually measured with a previously developed computer-aided tool. High statistical correlation with manually derived vessel diameters will determine the success of the process. The specific Phase I aims are to 1) Develop and validate a fully automatic retinal vessel extraction algorithm to segment the arteries and veins from a digital fundus photograph, 2) Develop and validate a vessel bifurcation and crossing recognition algorithm, and 3) Develop and validate a vessel diameter measurement algorithm. In Phase II the Auto-Cal tool will be used in a comparative study to examine 400 pre-proliferative diabetic retinopathy patients. The vessels have been manually measured. These data will be used to validate the system against a large "ground truth" population.