An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images

• Type: Article in International Scientific Journal
• Date: 2014

Authors

Behdad Dashtbozorg (Author) Other; Ana Maria Mendonca (Author) FEUP; Aurelio Campilho (Author) FEUP

Journal

Title: IEEE Transactions on Image Processing

Vol. 23

Pages: 1073-1083

ISSN: 1057-7149

Publisher: IEEE

Indexing

ISI Web of Knowledge - 122 Citations

Scopus - 148 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Technological sciences > Engineering > Biomedical enginnering

Other information

Authenticus ID: P-009-5AT

DOI: 10.1109/tip.2013.2263809

Abstract (EN): The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIREAVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11