Combining Feature Correspondence with Parametric Chamfer Alignment:
Hybrid Two-Stage Registration for Ultra-Widefield Retinal Images
Abstract
We propose a novel hybrid framework for registering retinal images in
the presence of extreme geometric distortions that are commonly
encountered in ultra-widefield (UWF) fluorescein angiography. Our
approach consists of two stages: a feature-based global registration and
a vessel-based local refinement. For the global registration, we
introduce a modified RANSAC algorithm that jointly identifies robust
matches between feature keypoints in reference and target images and
estimates a polynomial geometric transformation consistent with the
identified correspondences. Our RANSAC modification particularly
improves feature point matching and the registration in peripheral
regions that are most severely impacted by the geometric distortions.
The second local refinement stage is formulated in our framework as a
parametric chamfer alignment for vessel maps obtained using a deep
neural network. Because the complete vessel maps contribute to the
chamfer alignment, this approach not only improves registration accuracy
but also aligns with clinical practice, where vessels are typically a
key focus of examinations. We validate the effectiveness of the proposed
framework on a new UWF fluorescein angiography (FA) dataset and on the
existing narrow-field FIRE (fundus image registration) dataset and
demonstrate that it significantly outperforms prior retinal image
registration methods. The proposed approach enhances the utility of
large sets of longitudinal UWF images by enabling: (a) automatic
computation of vessel change metrics and (b) standardized and
co-registered examination that can better highlight changes of clinical
interest to physicians.