Removing mismatches for retinal image registration via multi-attribute-driven regularized mixture model

In order to address the problem of retinal image registration, this paper proposes and analyzes a novel and general matching algorithm called Multi-Attribute-Driven Regularized Mixture Model (MAD-RMM). Mismatches removal can play a key role in image registration, which refers to establish reliable matches between two point sets. Here the presented approach starts from multi-feature attributes which are used to guide the feature matching to identify inliers (correct matches) from outliers (incorrect matches), and then estimates the spatial transformation. In this paper, motivated by the problem of feature matching that the initial correspondence is always contaminated by outliers, thereby we formulate this issue as a probability deformable mixture model which consists of Gaussian components for inliers and uniform components for outliers. Moreover, the algorithm takes full advantage of using multiple attributes for better general matching performance. Here we are assuming all inliers are mapped into a high-dimensional feature space, namely reproducing kernel Hilbert space (RKHS), and the closed-form solution to the mapping function is given by the representation theorem with L2 norm regularization under the Expectation-Maximization (EM) algorithm. Finally, we evaluate the performance of the algorithm by applying it to retinal image registration on several datasets, where experimental results demonstrate that the MAD-RMM outperforms current state-of-the-art methods and shows the robustness to outliers on real retinal images.