Abstract:
Hypertension is a syndrome that causes multiple adverse effects on the human eye such as retinopathy, optic neuropathy and choroidopathy. Hypertensive retinopathy (HR) is a pathological condition of human retina that arises due to the elevated blood pressure. Increased blood pressure causes narrowing of retinal arteries and veins that may lead to retinal hemorrhages, arteriovenous nipping, cotton wool spots and even papilledema. HR is one of those syndromes that have no apparent symptoms and can cause severe damage to the vision or even death (due to the presence of severe papilledema). HR is classified into different grades or categories depending upon its severity. There are different eye testing techniques that can be used to detect HR. Some commonly used techniques are ophthalmoscopy, fundus photography and fundus fluorescein angiography (FFA). Different researchers are working on developing fully automated decision support systems to diagnose HR. But in order to verify their efficiency, they need to test them on standardized and publicly available datasets. Therefore, this paper proposes a dataset that contains 100 high quality digital retinal images for the automated diagnosis and grading of HR. The dataset has been acquired by Armed Forces Institute of Ophthalmology (AFIO) and it has been annotated by multiple expert ophthalmologists. The proposed dataset is first of its kind to provide researchers with the segmented arteriolar and venule patterns along with the clinically calculated arteriovenous ratios (AVR), optic nerve head (ONH), hard exudates (HE) and cotton wool spots annotations. Apart from this, the proposed dataset has been thoroughly compared with the well-known publicly available datasets where the proposed dataset outmatched other datasets by allowing different researchers to automatically diagnose different complications of retinal pathology.
