Paper Info
Title
Automatic detection and characterisation of retinal vessel tree bifurcations and crossovers in eye fundus images.
Abstract
Analysis of retinal vessel tree characteristics is an important task in medical diagnosis, specially in cases of diseases like vessel occlusion, hypertension or diabetes. The detection and classification of feature points in the arteriovenous eye tree will increase the information about the structure allowing its use for medical diagnosis. In this work a method for detection and classification of retinal vessel tree feature points is presented. The method applies and combines imaging techniques such as filters or morphologic operations to obtain an adequate structure for the detection. Classification is performed by analysing the feature points environment. Detection and classification of feature points is validated using the VARIA database. Experimental results are compared to previous approaches showing a much higher specificity in the characterisation of feature points while slightly increasing the sensitivity. These results provide a more reliable methodology for retinal structure analysis.
Year
DOI
Venue
2011
10.1016/j.cmpb.2010.06.002
Computer Methods and Programs in Biomedicine
Keywords
Field
DocType
feature point,adequate structure,retinal vessel tree feature,medical diagnosis,retinal vessel tree characteristic,retinal vessel tree bifurcation,retinal structure analysis,automatic detection,varia database,feature points environment,eye fundus image,vessel occlusion,arteriovenous eye tree,eye fundus,segmentation,structure analysis
Computer vision,Occlusion,Retinal structure,Computer science,Segmentation,Fundus (eye),Image processing,Artificial intelligence,Retinal,Medical diagnosis
Journal
Volume
Issue
ISSN
103
1
1872-7565
Citations 
PageRank 
References 
27
1.12
11
Authors
4
Name
Order
Citations
PageRank
David Calvo1473.72
M Ortega223537.13
Manuel G. Penedo318535.91
Jose Rouco45510.41