Mimetic finite difference methods for restoration of fundus images for automatic detection of glaucoma suspects

Abstract

ABSTRACT Glaucoma is one of the leading causes of irreversible blindness in people over 40 years old. Previously, it was developed acomputational tool for automatic glaucoma detection, which implements anovel method that has shown improvement in the accuracy of the detection compared to other classical methods. However, this method is sensitive to the quality of the acquired image. For this reason, automatic image restoration of the source images is needed to improve the quality of glaucoma suspect detection. We propose to use the Perona–Malik anisotropic diffusion filter as part of the pre-processing step because apart from attenuating noise and brilliance in the images, it preserves essential information such as the borders of the optic disc and the cup boundaries, which are of particular interest in this work. We solve numerically the problem in partial differential equations, which represent the Perona–Malik diffusion filter, using explicit finite mimetic differences methods, which have the advantage of preserving the continuum properties of the mathematical operators often encountered in image processing and analysis equations, ensuring better orders of convergence. By guaranteeing these mathematical properties, the original structure of the source image is maintained, improving the diagnosis of the patient.