In the presence of maculopathies, due to structural changes in the macula region, the fovea is usually located in pathological fundus images using normative anatomical measures (NAM). This simple method relies on two conditions: that images are acquired under standard testing conditions (primary head position and central fixation) and that the optic disk is visible entirely on the image. However, these two conditions are not always met in the case of maculopathies, en particulier lors de taches de fixations. Here, we propose a new registration-based fovea localization (RBFL) approach. The spatial relationship between fovea location and vessel characteristics (density and direction) is learned from 840 annotated healthy fundus images and then used to predict the precise fovea location in new images. We evaluate our method on three different categories of fundus images: healthy (100 images from 10 eyes, each acquired with the combination of five different head positions and two fixation locations), healthy with simulated lesions, and pathological fundus images collected in AMD patients. Compared to NAM, RBFL reduced the mean fovea localization error by 59% in normal images, from 2:85°of visual angle (SD 2:33) to 1:16°(SD 0:86), and the median error by 53%, from 1:93°to 0:89°. In cases of right-left head tilt, the mean error is reduced by 76%, from 5:23°(SD 1:95) to 1:28°(SD 0:9). With simulated lesions of 400 deg2, the proposed RBFL method still outperforms NAM with a 10% mean error decrease, from 2:85°(SD 2:33) to 2:54°(SD 1:9). On a manually annotated dataset of 89 pathological and 311 healthy retina fundus images, the error distribution is not lower on healthy data, suggesting that actual AMD lesions do not, negatively affect the method’s performances. The vascular structure provides enough information to precisely locate the fovea in fundus images in a way that is robust to head tilt, eccentric fixation location, missing vessels, and real macular lesions.