Cross-modal Fundus Image Registration under Large FoV Disparity

• URL: http://arxiv.org/abs/2512.12657v1
• Date: Sun, 14 Dec 2025 12:10:37 GMT
• Title: Cross-modal Fundus Image Registration under Large FoV Disparity
• Authors: Hongyang Li, Junyi Tao, Qijie Wei, Ningzhi Yang, Meng Wang, Weihong Yu, Xirong Li,
• Abstract summary: Previous work on cross-modal fundus image registration (CMFIR) assumes small cross-modal Field-of-View (FoV) disparity.<n>This paper is targeted at a more challenging scenario with large FoV disparity, to which directly applying current methods fails.<n>We propose Crop and Alignment for cross-modal fundus image Registration(CARe), a very simple yet effective method.
• Score: 18.69938492903591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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