Automated retinal image analysis holds prime significance in the accurate diagnosis of
various critical eye diseases that include diabetic retinopathy (DR), age-related macular …

This work examined recent literature on digital image processing in the field of diabetic
retinopathy. Algorithms were categorized into 5 steps (preprocessing; localization and …

Unpaired medical image enhancement (UMIE) aims to transform a low-quality (LQ) medical
image into a high-quality (HQ) one without relying on paired images for training. While most …

This paper presents a robust and fully automatic filter-based approach for retinal vessel
segmentation. We propose new filters based on 3D rotating frames in so-called orientation …

In the framework of computer-aided diagnosis of eye diseases, retinal vessel segmentation
based on line operators is proposed. A line detector, previously used in mammography, is …

Objective: Many common eye diseases and cardiovascular diseases can be diagnosed
through retinal imaging. However, due to uneven illumination, image blurring, and low …

A new objective non-reference quality metric for the performance of pixel level image fusion
is proposed in this paper. The novel metric employs a luminance model that is widely used …

Diabetic macular edema (DME) is a common vision threatening complication of diabetic
retinopathy. In a large scale screening environment DME can be assessed by detecting …

Digital images used in the field of ophthalmology are among the most important methods for
automatic detection of certain eye diseases. These processes include image enhancement …

Retinal fundus images are widely used for the clinical screening and diagnosis of eye
diseases. However, fundus images captured by operators with various levels of experience …