Cross-modal Fundus Image Registration under Large FoV Disparity

TL;DR

This paper introduces CARe, a simple yet effective method for cross-modal fundus image registration under large FoV disparity, using cropping based on retinal structure and a double-fitting alignment to improve registration accuracy.

Contribution

The paper presents a novel approach combining cropping and double-fitting alignment to handle large FoV disparities in cross-modal fundus image registration.

Findings

Effective registration on a new test set of 60 image pairs.

Outperforms existing methods in large FoV disparity scenarios.

Validates the approach's robustness and accuracy.

Abstract

Previous work on cross-modal fundus image registration (CMFIR) assumes small cross-modal Field-of-View (FoV) disparity. By contrast, this paper is targeted at a more challenging scenario with large FoV disparity, to which directly applying current methods fails. We propose Crop and Alignment for cross-modal fundus image Registration(CARe), a very simple yet effective method. Specifically, given an OCTA with smaller FoV as a source image and a wide-field color fundus photograph (wfCFP) as a target image, our Crop operation exploits the physiological structure of the retina to crop from the target image a sub-image with its FoV roughly aligned with that of the source. This operation allows us to re-purpose the previous small-FoV-disparity oriented methods for subsequent image registration. Moreover, we improve spatial transformation by a double-fitting based Alignment module that utilizes the classical RANSAC algorithm and polynomial-based coordinate fitting in a sequential manner. Extensive experiments on a newly developed test set of 60 OCTA-wfCFP pairs verify the viability of CARe for CMFIR.