Retinal Vessel Measurement and Characterization System

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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.