Segmentation of Retinal Arteries in Adaptive Optics Images

In this paper, we present a method for automatically segmenting the walls of retinal arteries in adaptive optics images. To the best of our knowledge, this is the first method addressing this problem in such images. To achieve this goal, we propose to model these walls as four curves approximately parallel to a common reference line located near the center of vessels. Once this line is detected, the curves are simultaneously positioned as close as possible to the borders of walls using an original tracking procedure to cope with deformations along vessels. Then, their positioning is refined using a deformable model embedding a parallelism constraint. Such an approach enables us to control the distance of the curves to their reference line and improve the robustness to image noise. This model was evaluated on healthy subjects by comparing the results against segmentations from physicians. Noticeably, the error introduced by this model is smaller or very near the inter-physicians error.