Retinal vasculature is affected in many ocular conditions including diabetic retinopathy, glaucoma and age-related macular degeneration and these alterations can be used as biomarkers. Therefore, it is important to segment and quantify retinal blood vessel characteristics (RBVC) accurately. Using a new automated image processing method applied to optical coherence tomography angiography (OCTA) images we computed the RBVC and compared them between emmetropic (n=40) and ametropic (n=97) subjects. All 137 OCTA images had dimensions of 420x420 pixels corresponding to 6mm x 6mm. The myopia OCTA images were labelled based on a severity scale as mild, moderate, high and very high using standard refractive error classifications. Before image processing, all the images were cropped to 210 X 210 pixels keeping the foveal avascular zone (FAZ) at the centre to quantify the RBVC. The mean ± standard deviation of the Grisan index, a measure of retinal blood vessel tortuosity in the emmetropic, and myopic eye were 0.05 ± 0.02 and 0.05 ± 0.03 respectively. The total vessel distance measures were calculated and the largest were found in emmetropic eyes (45.95 ± 19.54) and shortest in myopic eyes (6.50 ± 5.17). The total number of turning points and inflection points were found to be statistically significant (p<0.05) between control and myopic eyes. However, other RBVC parameters were not statistically different (p=<0.05). We found qualitatively that RBVC changes with increasing severity of the refractive power. Among RBVC parameters, average number of turning points (NTP) decreasing trend with degree of myopia increases.
Uniform and quantitative grading of retinal vessel characteristics are replacing subjective and qualitative schemes. However clinically accurate blood vessel extraction is very important. The tortuosity of these vessels is an important metric to study the curvature variations in normal and diseased eyes. In this study we provide a new unsupervised and fully automated approach for studying curvature variation of the blood vessels. We then pro- vide tortuosity quantification of these extracted vessels. In this study we used optical coherence tomography angiographic fundus images of dimensions 420x420 pixels corresponding to 6mm x 6mm were used in this study. We focused on the central circular 210x210 pixel region around the foveal avascular zone (FAZ) for tortuosity quantification. Our segmentation approach starts with a 3mm x 3mm central circular region extraction surrounding the FAZ. We then use a multi-scale, multi-span line detection filter to smoothen out the high noise in the background and at the same time increase the intensity of target vessels. This is followed by a K-means procedure to filter out the noise and target vessels into two categories. Next steps are morphological closing and noise removal and iterative erosion of pixels to skeletonize the vessels. The final extracted vessels are of the form of single pixel piecewise continuous fragments. These are finer than human annotations and at the same time free of noise. We then provide accurate standard tortuosity measures - Distance Measure, Inflection Points, Turning Points, etc. for these OCTA images using the extracted vessels through mathematical modelling.
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