Multimodal retinal image registration using edge map and feature guided Gaussian mixture model

Abstract

In this paper, we propose a method for multimodal retinal image registration based on feature guided Gaussian mixture model (GMM) and edge map. We extract two sets of feature points from the edge maps of two images, and formulate image registration as the estimation of a feature guided mixture of densities: a GMM is fitted to one point set, such that both the centers and local features of the Gaussian densities are constrained to coincide with the other point set. The problem is solved under a maximum-likelihood framework together with an iterative EM algorithm initialized by confident feature matches, where the image transformation is modeled by an affine function. Extensive experiments on various retinal images show the robustness of our method, which consistently outperforms other state-of-the-arts, especially when the data is badly degraded.