Purpose The Arteriolar-to-Venular diameter Ratio (AVR), a parameter derived from vessel caliber measurements in a specific region of retinal images, is used as a descriptor of generalized arteriolar narrowing. We developed a computerized system to compute AVR in a totally automatic way. Methods Images are at first enhanced to highlight the vessel network, which is then traced by a vessel tracking algorithm. From the detected vessel structure, the position of the optic disc is derived and the region inside which the AVR data are to be measured is determined. Vessels within this region are classified as either arteries or veins, their caliber estimated and the AVR parameter is eventually computed. Results provided by the system have been compared with manually derived AVR values on 20 eye fundus images. Results Mean and SD values from the two sets of measurements are the same, and automatic/manual ratios have an average value of 1 and 95% confidence interval of (0.98-1.02). The correlation coefficient between the two methods is 0.88. In only two images the ratio is appreciably different from unity, 1.14 and 0.89 respectively. A detailed analysis of these cases revealed that in each image the wrong classification of one vessel only was the cause for these unsatisfactory results. When these misclassifications are manually corrected, e.g. with a quick editing tool that can be easily made available to the user, the ratios become 0.95 and 0.99, respectively, and the overall correlation coefficient becomes 0.97. Conclusion Additional evaluation on a larger set of images, acquired from subjects exhibiting wide variations of AVR, will be performed in order to fully assess the reliability and clinical applicability of this automatic procedure.
Automatic estimation of the arteriolar-to-venular diameter ratio (AVR) in retinal images
RUGGERI, ALFREDO;TRAMONTAN, LARA;GRISAN, ENRICO
2008
Abstract
Purpose The Arteriolar-to-Venular diameter Ratio (AVR), a parameter derived from vessel caliber measurements in a specific region of retinal images, is used as a descriptor of generalized arteriolar narrowing. We developed a computerized system to compute AVR in a totally automatic way. Methods Images are at first enhanced to highlight the vessel network, which is then traced by a vessel tracking algorithm. From the detected vessel structure, the position of the optic disc is derived and the region inside which the AVR data are to be measured is determined. Vessels within this region are classified as either arteries or veins, their caliber estimated and the AVR parameter is eventually computed. Results provided by the system have been compared with manually derived AVR values on 20 eye fundus images. Results Mean and SD values from the two sets of measurements are the same, and automatic/manual ratios have an average value of 1 and 95% confidence interval of (0.98-1.02). The correlation coefficient between the two methods is 0.88. In only two images the ratio is appreciably different from unity, 1.14 and 0.89 respectively. A detailed analysis of these cases revealed that in each image the wrong classification of one vessel only was the cause for these unsatisfactory results. When these misclassifications are manually corrected, e.g. with a quick editing tool that can be easily made available to the user, the ratios become 0.95 and 0.99, respectively, and the overall correlation coefficient becomes 0.97. Conclusion Additional evaluation on a larger set of images, acquired from subjects exhibiting wide variations of AVR, will be performed in order to fully assess the reliability and clinical applicability of this automatic procedure.
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