Retinal vessel landmark detection using deep learning and hessian matrix

Abstract

The purpose of retinal image registration is to establish the coherent correspondences between the multi-model retinal image for applying into the ophthalmological surgery. Vessel landmarks detection in retinal image is the vital step in the retinal image registration. In this paper, a novel approach is proposed, firstly, a deep learning technology is used to vessel segmentation to generate the probability map of the retinal image, which is more reliable for optimizing the feature detection in retinal image. Secondly, we detect the landmarks using the multi-scale Hessian response on the probability map of the retinal image. Compared to the traditional methods, the results show that our method enable a majority of the bifurcation points, crossover points and curvature extreme points to be detected out simultaneously. Moreover, the impact of image noise and pathology can be reduced significantly.