EFPT-OIDS: Evaluation Framework for a Pre-processing Techniques of Automatic Optho-Imaging Diagnosis and Detection System

Abstract

The modalities of FUNDUS images and the availability of public domain data sets provides a starting point in designing an ecosystem for developing an automatic detection of degenerative early-stage Glaucoma and Diabetic Retinopathy, and other eye-related diseases. The existing techniques for these operations lack flexibility and robustness in their design implementation and are limited to only certain preprocessing requirements. However, the existing methods are useful but provide lower performance when the FUNDUS image quality degrades due to misalignment of lens opening in camera and poor functioning of visual sensors. This paper presents a unified framework that mechanizes different preprocessing techniques to benefit the Optho-imaging diagnosis and disease detection process. The proposed framework facilitates on-demand data treatment operations that include image interpolation, brightness adjustment, illumination correction, and noise reduction. The proposed techniques for FUNDUS image enhancement provide better PSNR and SSIM-performance metrics for image quality than existing popular image enhancement techniques when tested on two standard publicly available datasets. The contribution of the proposed framework is that it offers flexible and effective mechanisms that meet dynamic preprocessing operations on an on-demand basis to prepare better data representation for building machine learning models. The framework can also be used in real-time for eye disease diagnosis by an ophthalmologist.