Multiple Image Features-Based Retinal Image Registration Using Global and Local Geometric Structure Constraints

Retinal image registration is a key step in treating hypertension, diabetes and various retinal global diseases. In current methods of retinal image registration, they generally suffer from a lack of reliable features, missing true correspondences and geometric distortion. To address above problem, we propose a robust non-rigid retinal image registration method using multi-image features and dual constraints (i.e. the global and local geometric structure constraints). Our method contains the following main contributions. (i) A finite mixture model based on multi-feature is constructed for handling different types of image features. (ii) A combination of three features is substituted into the mixture model to improve the complementarities of different features. (iii) Dual constraints are proposed for ensuring the stability of the global and local structures of feature sets in the process of spatial transformation and updating. The performance of our method is evaluated by four main types of retinal images, which shows our method outperforms five state-of-the-art methods in most scenarios, especially when the retinal image has a large angle change.