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

Retinal vessel segmentation algorithms are a fundamental component of automatic retinal
disease screening systems. This work examines the blood vessel segmentation …

Automated detection of curvilinear structures, eg, blood vessels or nerve fibres, from medical
and biomedical images is a crucial early step in automatic image interpretation associated to …

Objective: Deep learning based methods for retinal vessel segmentation are usually trained
based on pixel-wise losses, which treat all vessel pixels with equal importance in pixel-to …

Automatic retinal vessel segmentation is a fundamental step in the diagnosis of eye-related
diseases, in which both thick vessels and thin vessels are important features for symptom …

Retinal blood vessel segmentation in fundus images plays an important role in the early
diagnosis and treatment of retinal diseases. In recent years, the segmentation methods …

Goal: In this work, we present an extensive description and evaluation of our method for
blood vessel segmentation in fundus images based on a discriminatively trained fully …

This paper presents a new supervised method for vessel segmentation in retinal images.
This method remolds the task of segmentation as a problem of cross-modality data …

Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical
pathologies. The automatic segmentation of the vessel tree is an important pre-processing …

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 …