Nonrigid Multimodal Registration Based on Fuzzy Inference System for Retinal Image Registration
Background: Retinal imaging employs various modalities, each providing distinct perspectives on ocular structures. However, the integration of information from these modalities, which often have differing resolutions, requires effective image registration techniques. Existing retinal image registrat...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2025-05-01
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| Series: | Journal of Medical Signals and Sensors |
| Subjects: | |
| Online Access: | https://journals.lww.com/jmss/fulltext/2025/05010/nonrigid_multimodal_registration_based_on_fuzzy.1.aspx |
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| _version_ | 1849705504932102144 |
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| author | Monire Sheikh Hosseini Hossein Rabbani |
| author_facet | Monire Sheikh Hosseini Hossein Rabbani |
| author_sort | Monire Sheikh Hosseini |
| collection | DOAJ |
| description | Background:
Retinal imaging employs various modalities, each providing distinct perspectives on ocular structures. However, the integration of information from these modalities, which often have differing resolutions, requires effective image registration techniques. Existing retinal image registration methods typically rely on rigid or affine transformations, which may not adequately address the complexities of multimodal retinal images.
Method:
This study introduces a nonrigid fuzzy image registration approach designed to align optical coherence tomography (OCT) images with fundus images. The method employs a fuzzy inference system (FIS) that uses vessel locations as key features for registration. The FIS applies specific rules to map points from the source image to the reference image, facilitating accurate alignment.
Results:
The proposed method achieved a mean absolute registration error of 44.57 ± 39.38 µm in the superior–inferior orientation and 11.46 ± 10.06 µm in the nasal-temporal orientation. These results underscore the method’s precision in aligning multimodal retinal images.
Conclusion:
The nonrigid fuzzy image registration approach demonstrates robust and versatile performance in integrating multimodal retinal imaging data. Despite its straightforward implementation, the method effectively addresses the challenges of multimodal retinal image registration, providing a reliable tool for advanced ocular imaging analysis. |
| format | Article |
| id | doaj-art-9551cf41721a4e058e52d3d1c4e287fb |
| institution | DOAJ |
| issn | 2228-7477 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Medical Signals and Sensors |
| spelling | doaj-art-9551cf41721a4e058e52d3d1c4e287fb2025-08-20T03:16:28ZengWolters Kluwer Medknow PublicationsJournal of Medical Signals and Sensors2228-74772025-05-01155131310.4103/jmss.jmss_42_24Nonrigid Multimodal Registration Based on Fuzzy Inference System for Retinal Image RegistrationMonire Sheikh HosseiniHossein RabbaniBackground: Retinal imaging employs various modalities, each providing distinct perspectives on ocular structures. However, the integration of information from these modalities, which often have differing resolutions, requires effective image registration techniques. Existing retinal image registration methods typically rely on rigid or affine transformations, which may not adequately address the complexities of multimodal retinal images. Method: This study introduces a nonrigid fuzzy image registration approach designed to align optical coherence tomography (OCT) images with fundus images. The method employs a fuzzy inference system (FIS) that uses vessel locations as key features for registration. The FIS applies specific rules to map points from the source image to the reference image, facilitating accurate alignment. Results: The proposed method achieved a mean absolute registration error of 44.57 ± 39.38 µm in the superior–inferior orientation and 11.46 ± 10.06 µm in the nasal-temporal orientation. These results underscore the method’s precision in aligning multimodal retinal images. Conclusion: The nonrigid fuzzy image registration approach demonstrates robust and versatile performance in integrating multimodal retinal imaging data. Despite its straightforward implementation, the method effectively addresses the challenges of multimodal retinal image registration, providing a reliable tool for advanced ocular imaging analysis.https://journals.lww.com/jmss/fulltext/2025/05010/nonrigid_multimodal_registration_based_on_fuzzy.1.aspxfundusfuzzymultimodal registrationnonrigid image registrationoptical coherence tomography |
| spellingShingle | Monire Sheikh Hosseini Hossein Rabbani Nonrigid Multimodal Registration Based on Fuzzy Inference System for Retinal Image Registration Journal of Medical Signals and Sensors fundus fuzzy multimodal registration nonrigid image registration optical coherence tomography |
| title | Nonrigid Multimodal Registration Based on Fuzzy Inference System for Retinal Image Registration |
| title_full | Nonrigid Multimodal Registration Based on Fuzzy Inference System for Retinal Image Registration |
| title_fullStr | Nonrigid Multimodal Registration Based on Fuzzy Inference System for Retinal Image Registration |
| title_full_unstemmed | Nonrigid Multimodal Registration Based on Fuzzy Inference System for Retinal Image Registration |
| title_short | Nonrigid Multimodal Registration Based on Fuzzy Inference System for Retinal Image Registration |
| title_sort | nonrigid multimodal registration based on fuzzy inference system for retinal image registration |
| topic | fundus fuzzy multimodal registration nonrigid image registration optical coherence tomography |
| url | https://journals.lww.com/jmss/fulltext/2025/05010/nonrigid_multimodal_registration_based_on_fuzzy.1.aspx |
| work_keys_str_mv | AT moniresheikhhosseini nonrigidmultimodalregistrationbasedonfuzzyinferencesystemforretinalimageregistration AT hosseinrabbani nonrigidmultimodalregistrationbasedonfuzzyinferencesystemforretinalimageregistration |