Extracting Vessel Centerlines From Retinal Images Using Topographical Properties and Directional Filters

LABORATORY OF MATHEMATICS IN IMAGING Algorithms for Extracting Vessel Centerlines

1 The detection of the vessels centerlines is a useful preprocessing step for 3D quantification of stenosis, topological representation of the vessel tree and registration with an atlas, virtual endoscopy, and visualization of the vascular network. We propose to compare three classes of algorithms leading to centerline representations of the vessels. The first one relies on a pre-segmentation o...

Unsupervised Retinal Vessel Segmentation Using Combined Filters.

Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enh...

Vessel Delineation in Retinal Images using Leung-Malik filters and Two Levels Hierarchical Learning

Blood vessel segmentation is important for the analysis of ocular fundus images for diseases affecting vessel caliber, occlusion, leakage, inflammation, and proliferation. We introduce a novel supervised method to evaluate performance of Leung-Malik filters in delineating vessels. First, feature vectors are extracted for every pixel with respect to the response of Leung-Malik filters on green c...

Vessel Segmentation in Retinal Images Using Graph-Theoretical Vessel Tracking

This paper presents a method for automatic segmentation of blood vessels in retinal images. The method is based on vessel tracking technique. The key idea of the method is that first a set of seed points (center of vessel cross sections) is extracted. Then, the seed points are connected to establish the vessel skeleton. Finally, the false vessel point are rejected by resorting to a hypothesis-v...

Vessel Junction Detection From Retinal Images

This paper presents a perceptual organization based method for detecting Vessel Junctions (VJs) from retinal images. A retinal image is first segmented into edge traces which contain vessel boundaries. Each trace is divided into generic curve segments (GCSs) at curve partitioning points (CPPs). CPPs are the places on a trace from where the continuity of GCSs was broken according to perceptual o...

Blood Vessel Segmentation from Retinal Images