Fundus cameras are widely used by ophthalmologists for monitoring and diagnosing retinal pathologies. Unfortunately, no optical system is perfect, and the visibility of retinal images can be greatly degraded due to the presence of problematic illumination, intraocular scattering, or blurriness caused by sudden movements. To improve image quality, different retinal image restoration/enhancement techniques have been developed, which play an important role in improving the performance of various clinical and computer-assisted applications. This paper gives a comprehensive review of these restoration/enhancement techniques, discusses their underlying mathematical models, and shows how they may be effectively applied in real-life practice to increase the visual quality of retinal images for potential clinical applications including diagnosis and retinal structure recognition. All three main topics of retinal image restoration/enhancement techniques, i.e., illumination correction, dehazing, and deblurring, are addressed. Finally, some considerations about challenges and the future scope of retinal image restoration/enhancement techniques will be discussed.

Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

OBJECTIVE: To determine the association between retinal vasculature changes and stroke.
METHODS: MEDLINE and EMBASE were searched for relevant human studies to September 2015 that investigated the association between retinal vasculature changes and the prevalence or incidence of stroke; the studies were independently examined for their qualities. Data on clinical characteristics and calculated summary odds ratios (ORs) were extracted for associations between retinal microvascular abnormalities and stroke, including stroke subtypes where possible, and adjusted for key variables.
RESULTS: Nine cases were included in the study comprising 20 659 patients, 1178 of whom were stroke patients. The retinal microvascular morphological markers used were hemorrhage, microaneurysm, vessel caliber, arteriovenous nicking, and fractal dimension. OR of retinal arteriole narrowing and retinal arteriovenous nicking and stroke was 1.42 and 1.91, respectively, indicating that a small-caliber retinal arteriole and retinal arteriovenous nicking were associated with stroke. OR of retinal hemorrhage and retinal microaneurysm and stroke was 3.21 and 3.83, respectively, indicating that retinal microvascular lesions were highly associated with stroke. Results also showed that retinal fractal dimension reduction was associated with stroke (OR: 2.28 for arteriole network, OR: 1.80 for venular network).
CONCLUSIONS: Retinal vasculature changes have a specific relationship to stroke, which is promising evidence for the prediction of stroke using computerized retinal vessel analysis.