Image processing pipeline for the detection of blood flow through retinal vessels with subpixel accuracy in fundus images.

Abstract Background and Objective Blood flow detection through the retinal vessels is an essential step in diagnosing several eye diseases. It manifests itself as changes in vessel diameter in consecutive phases of blood flow. Previous studies focused mainly on determining retinal vessel diameter by the manual demarcation of vessel edges, which is time-consuming. As a result, only a few selected vessels were considered, which is not reliable. Such measurements are also prone to human errors and operator subjectivity, which additionally decreases their reliability. For these reasons, this paper proposes an automated pipeline to analyze the blood flow through retinal vessels. Methods Convolutional neural networks were used for optic disc and vessel detection and full width at half maximum analysis used for vessel width assessment at the subpixel level. Measurements of the diameter were performed for five phases of the blood flow to all vessels at a particular distance derived from the optic disc size. We tested the approach on fundus images of five patients, with both eyes examined in each participant. The threshold for the detections of blood flow was when the retinal diameter vessel measurements were above the camera's resolution as compared among all 5 phases of blood flow. Results A total of 205 large caliber blood vessels were analyzed with blood flow detected in 18 retinal blood vessels. Conclusions Average vessel diameters derived from manual and automatic measurements differed on average by 4.96%. Average relative errors for single vessel measurements along the vessels range from 4.21 to 11.85%, with a global average at the level of 8%. Therefore, the measurements can be considered as accurate and in a high agreement between the expert and algorithm