Detection of hypertensive retinopathy using vessel measurements and textural features

• Published in: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2014; pp. 5406 - 5409
• Main Authors: Agurto, Carla, Joshi, Vinayak, Nemeth, Sheila, Soliz, Peter, Barriga, Simon
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2014
• Subjects: Arteries; Arteries - abnormalities; Case-Control Studies; Color; Databases as Topic; Feature extraction; Heart rate; Humans; Hypertensive Retinopathy - diagnosis; Image color analysis; Image Processing, Computer-Assisted; Image segmentation; Joint Instability - diagnosis; Optic Disk - pathology; Retina; Retinal Vessels - pathology; ROC Curve; Skin Diseases, Genetic - diagnosis; Vascular Malformations - diagnosis; Veins
• ISSN: 1094-687X; 1557-170X
• DOI: 10.1109/EMBC.2014.6944848

Features that indicate hypertensive retinopathy have been well described in the medical literature. This paper presents a new system to automatically classify subjects with hypertensive retinopathy (HR) using digital color fundus images. Our method consists of the following steps: 1) normalization and enhancement of the image; 2) determination of regions of interest based on automatic location of the optic disc; 3) segmentation of the retinal vasculature and measurement of vessel width and tortuosity; 4) extraction of color features; 5) classification of vessel segments as arteries or veins; 6) calculation of artery-vein ratios using the six widest (major) vessels for each category; 7) calculation of mean red intensity and saturation values for all arteries; 8) calculation of amplitude-modulation frequency-modulation (AM-FM) features for entire image; and 9) classification of features into HR and non-HR using linear regression. This approach was tested on 74 digital color fundus photographs taken with TOPCON and CANON retinal cameras using leave-one out cross validation. An area under the ROC curve (AUC) of 0.84 was achieved with sensitivity and specificity of 90% and 67%, respectively.