Enhanced visualization of retinal vasculature in fundus images through image processing

Abstract

PurposeTo develop a digital filter that enhances visualization of retinal blood vessels.MethodsFour‐hundred 24‐bit color fundus images were analyzed and properties of red, green, and blue channels were extracted. Then, using hemoglobin absorption coefficients, the relevant weights for gray‐scale conversion that emphasizes retinal vessels were calculated. To evaluate images, edges were detected via convolutional 2D Laplacian kernel from the processed images, and the number of edges, number of effective edges, and sum of intensities of edges were evaluated.ResultsThe values of weights for red, green, and blue channels were calculated to be −0.0572, 0.7335, and 2.2079, respectively. When comparing the images that were processed using the new digital filter based on these values with the original image, gray‐scale, green, red, blue, and green and blue digital filter images, the number of edges, effective edges, and sum of intensities of edges were all found to be significantly higher in the images processed with the new filter (P < 10–16).ConclusionsThe RGB filter developed here was based on actual fundus images. The hemoglobin absorbance reinforced the edges of retinal blood vessels, verifying that the new RGB filter can enhance the visualization of retinal vasculature.