Automated retinal vessel type classification in color fundus images

H. Yu; S. Barriga; C. Agurto; S. Nemeth; W. Bauman; P. Soliz

doi:10.1117/12.2006444

28 February 2013 Automated retinal vessel type classification in color fundus images

H. Yu, S. Barriga, C. Agurto, S. Nemeth, W. Bauman, P. Soliz

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86700P (2013) https://doi.org/10.1117/12.2006444
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

Citation Download Citation

H. Yu, S. Barriga, C. Agurto, S. Nemeth, W. Bauman, and P. Soliz "Automated retinal vessel type classification in color fundus images", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700P (28 February 2013); https://doi.org/10.1117/12.2006444

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