Cross-modal Fundus Image Registration under Large FoV Disparity
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912764050538496 |
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| author | Li, Hongyang Tao, Junyi Wei, Qijie Yang, Ningzhi Wang, Meng Yu, Weihong Li, Xirong |
| author_facet | Li, Hongyang Tao, Junyi Wei, Qijie Yang, Ningzhi Wang, Meng Yu, Weihong Li, Xirong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12657 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cross-modal Fundus Image Registration under Large FoV Disparity Li, Hongyang Tao, Junyi Wei, Qijie Yang, Ningzhi Wang, Meng Yu, Weihong Li, Xirong Computer Vision and Pattern Recognition 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. |
| title | Cross-modal Fundus Image Registration under Large FoV Disparity |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.12657 |