Automatic Detection of Vascular Bifurcations and Crossovers from Color Retinal Fundus Images

Authors:
Alauddin Bhuiyan;Baikunth Nath;Joselito Chua;Kotagiri Ramamohanarao
Affiliations:
-;-;-;-
Venue:
SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
Year:
2007

Citing 0
Cited 7

Automatic detection and characterisation of retinal vessel tree bifurcations and crossovers in eye fundus images

Computer Methods and Programs in Biomedicine
Detection of retinal vascular bifurcations by trainable V4-like filters

CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Automated Analysis of Retinal Vascular Tortuosity on Color Retinal Images

Journal of Medical Systems
Retinal image matching using hierarchical vascular features

Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
Junction detection for linear structures based on Hessian, correlation and shape information

Pattern Recognition
Detection of retinal vascular bifurcations by rotation- and scale-invariant COSFIRE filters

ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Identifying the vascular bifurcations and crossovers in the retinal image is helpful for predicting many cardiovascular diseases and can be used as biometric features and for image registration. In this paper, we propose an efficient method to detect vascular bifurcations and crossovers based on the vessel geometrical features. We segment the blood vessels from the color retinal RGB image, and apply the morphological thinning operation to find the vessel centerline. Applying a filter on this centreline image we detect the potential bifurcation and crossover points. The geometrical and topological properties of the blood vessels passing through these points are utilized to identify these points as the vessel bifurcations and crossovers. We evaluate our method against manually measured bifurcation and crossover points by an expert, and achieved the detection accuracy of 95.82%.