Consistent Point Matching
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908473861603328 |
|---|---|
| author | Yerebakan, Halid Ziya Valadez, Gerardo Hermosillo |
| author_facet | Yerebakan, Halid Ziya Valadez, Gerardo Hermosillo |
| contents | This study demonstrates that incorporating a consistency heuristic into the point-matching algorithm \cite{yerebakan2023hierarchical} improves robustness in matching anatomical locations across pairs of medical images. We validated our approach on diverse longitudinal internal and public datasets spanning CT and MRI modalities. Notably, it surpasses state-of-the-art results on the Deep Lesion Tracking dataset. Additionally, we show that the method effectively addresses landmark localization. The algorithm operates efficiently on standard CPU hardware and allows configurable trade-offs between speed and robustness. The method enables high-precision navigation between medical images without requiring a machine learning model or training data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23609 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Consistent Point Matching Yerebakan, Halid Ziya Valadez, Gerardo Hermosillo Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing Machine Learning This study demonstrates that incorporating a consistency heuristic into the point-matching algorithm \cite{yerebakan2023hierarchical} improves robustness in matching anatomical locations across pairs of medical images. We validated our approach on diverse longitudinal internal and public datasets spanning CT and MRI modalities. Notably, it surpasses state-of-the-art results on the Deep Lesion Tracking dataset. Additionally, we show that the method effectively addresses landmark localization. The algorithm operates efficiently on standard CPU hardware and allows configurable trade-offs between speed and robustness. The method enables high-precision navigation between medical images without requiring a machine learning model or training data. |
| title | Consistent Point Matching |
| topic | Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2507.23609 |