Accurate segmentation of retinal fundus vessel images is vital to clinical diagnosis. Due to
the intricate vascular morphology, high noise and low contrast of fundus vessel images …

The retinal images with lesions, exudates, non-uniformed illuminations and pathological
artifacts have intrinsic problems such as the absence of thin vessels and false vessels …

This work presents a curvelet-based algorithm for detection of optic disk (OD) and exudates
on low contrast images. This algorithm which is composed of three main stages does not …

In this paper, we present a novel modification to level set based automatic retinal
vasculature segmentation approaches. The method introduces ridge sample extraction for …

Retinal image analysis has been widely used for early detection and diagnosis of multiple
systemic diseases. Accurate vessel extraction in retinal image is a crucial step towards a …

Segmentation of vasculature in retinal fundus image by level set methods employing
classical edge detection methodologies is a tedious task. In this study, a revised level set …

Diabetic retinopathy is a major cause of blindness, and its earliest signs include damage to
the blood vessels and the formation of lesions in the retina. Automated detection and …

Diabetes mellitus, often known as diabetes, has been recognized as a reason for blindness.
As per the World Health Organization (WHO) report, 2200 million people worldwide are …

For timely diagnosis of retinal disease, routine retinal monitoring of people with high risk
should be put in place. To assist the ophthalmologists in performing retinal analysis …

This paper presents a new unsupervised method for segmenting blood vessels in digital
retinal images. The proposed method uses K-means clustering to binarize grayscale vessel …