Novel feature-based method for multi-modal biomedical image registration compared to intensity-based technique
Abstract Multimodal image registration plays a crucial role in biomedical research, enabling the integration of complementary information from different imaging techniques. We present a novel feature-based approach for multimodal image registration, alongside traditional intensity-based methods. Our...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-12862-2 |
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| author | Mohammad Javad Shojaei Lichen Yang Kazem Shojaei Jeerapat Doungchawee Richard W. Vachet |
| author_facet | Mohammad Javad Shojaei Lichen Yang Kazem Shojaei Jeerapat Doungchawee Richard W. Vachet |
| author_sort | Mohammad Javad Shojaei |
| collection | DOAJ |
| description | Abstract Multimodal image registration plays a crucial role in biomedical research, enabling the integration of complementary information from different imaging techniques. We present a novel feature-based approach for multimodal image registration, alongside traditional intensity-based methods. Our method, inspired by SPP-net architecture, employs multi-level feature extraction for robust image alignment. Additionally, we perform t-SNE dimensionality reduction on the MALDI-MSI dataset to enhance feature discrimination and visualization. We evaluated both approaches using datasets from the ANHIR Grand Challenge and mass spectrometry imaging modalities (LA-ICP-MS and MALDI-MSI). The proposed feature-based method achieved comparable accuracy to optimized intensity-based approaches, with Dice Coefficients of 0.95 for ANHIR samples (e.g., COAD_05) and 0.97 for mass spectrometry data, while requiring approximately 50% less computational time. Quantitative evaluation through Mutual Information metrics and Hausdorff Distance demonstrated high registration accuracy across different tissue types and imaging modalities. These results establish our feature-based approach as an efficient alternative to traditional intensity-based methods for multimodal image registration in biomedical applications. |
| format | Article |
| id | doaj-art-67def5749a4745c490f77f2ac1e982e4 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-67def5749a4745c490f77f2ac1e982e42025-08-20T03:05:18ZengNature PortfolioScientific Reports2045-23222025-08-0115111710.1038/s41598-025-12862-2Novel feature-based method for multi-modal biomedical image registration compared to intensity-based techniqueMohammad Javad Shojaei0Lichen Yang1Kazem Shojaei2Jeerapat Doungchawee3Richard W. Vachet4Department of Materials, Imperial College LondonSchool of Computing, Newcastle UniversitySchool of Medicine, Tehran University of Medical SciencesDepartment of Chemistry, University of Massachusetts AmherstDepartment of Chemistry, University of Massachusetts AmherstAbstract Multimodal image registration plays a crucial role in biomedical research, enabling the integration of complementary information from different imaging techniques. We present a novel feature-based approach for multimodal image registration, alongside traditional intensity-based methods. Our method, inspired by SPP-net architecture, employs multi-level feature extraction for robust image alignment. Additionally, we perform t-SNE dimensionality reduction on the MALDI-MSI dataset to enhance feature discrimination and visualization. We evaluated both approaches using datasets from the ANHIR Grand Challenge and mass spectrometry imaging modalities (LA-ICP-MS and MALDI-MSI). The proposed feature-based method achieved comparable accuracy to optimized intensity-based approaches, with Dice Coefficients of 0.95 for ANHIR samples (e.g., COAD_05) and 0.97 for mass spectrometry data, while requiring approximately 50% less computational time. Quantitative evaluation through Mutual Information metrics and Hausdorff Distance demonstrated high registration accuracy across different tissue types and imaging modalities. These results establish our feature-based approach as an efficient alternative to traditional intensity-based methods for multimodal image registration in biomedical applications.https://doi.org/10.1038/s41598-025-12862-2 |
| spellingShingle | Mohammad Javad Shojaei Lichen Yang Kazem Shojaei Jeerapat Doungchawee Richard W. Vachet Novel feature-based method for multi-modal biomedical image registration compared to intensity-based technique Scientific Reports |
| title | Novel feature-based method for multi-modal biomedical image registration compared to intensity-based technique |
| title_full | Novel feature-based method for multi-modal biomedical image registration compared to intensity-based technique |
| title_fullStr | Novel feature-based method for multi-modal biomedical image registration compared to intensity-based technique |
| title_full_unstemmed | Novel feature-based method for multi-modal biomedical image registration compared to intensity-based technique |
| title_short | Novel feature-based method for multi-modal biomedical image registration compared to intensity-based technique |
| title_sort | novel feature based method for multi modal biomedical image registration compared to intensity based technique |
| url | https://doi.org/10.1038/s41598-025-12862-2 |
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