Registration of retinal images from Public Health by minimising an error between vessels using an affine model with radial distortions
This improves registration accuracy for retinal images in public health applications, though it appears incremental as it builds on prior work with model refinements.
The paper tackles retinal image registration by estimating an affine model with radial distortions using vessel error minimization, achieving successful registration of 96% of 271 image pairs from a Public Health dataset, outperforming previous and state-of-the-art methods.
In order to estimate a registration model of eye fundus images made of an affinity and two radial distortions, we introduce an estimation criterion based on an error between the vessels. In [1], we estimated this model by minimising the error between characteristics points. In this paper, the detected vessels are selected using the circle and ellipse equations of the overlap area boundaries deduced from our model. Our method successfully registers 96 % of the 271 pairs in a Public Health dataset acquired mostly with different cameras. This is better than our previous method [1] and better than three other state-of-the-art methods. On a publicly available dataset, ours still better register the images than the reference method.