Coupled parallel snakes for segmenting healthy and pathological retinal arteries in adaptive optics images

Abstract

In this paper, we propose two important improvements of an existing approach for automatically segmenting the walls of retinal arteries of healthy/pathological subjects in adaptive optics images. We illustrate the limits of the previous approach and propose to (i) modify the pre-segmentation step, and (ii) embed additional information through coupling energy terms in the parallel active contour model. The interest of these new elements as well as the pre-segmentation step is then evaluated against manual segmentations. They improve the robustness against low contrasted walls and morphological deformations that occur along vessels in case of pathologies. Noticeably, this strategy permits to obtain a mean error of 13.4% compared to an inter-physicians error of 17%, for the wall thickness which is the most sensitive measure used. Additionally, this mean error is in the same range than for healthy subjects.