Sulcal Pattern Matching with the Wasserstein Distance
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866917430540894208 |
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| author | Chen, Zijian Das, Soumya Chung, Moo K. |
| author_facet | Chen, Zijian Das, Soumya Chung, Moo K. |
| contents | We present the unified computational framework for modeling the sulcal patterns of human brain obtained from the magnetic resonance images. The Wasserstein distance is used to align the sulcal patterns nonlinearly. These patterns are topologically different across subjects making the pattern matching a challenge. We work out the mathematical details and develop the gradient descent algorithms for estimating the deformation field. We further quantify the image registration performance. This method is applied in identifying the differences between male and female sulcal patterns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_00385 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sulcal Pattern Matching with the Wasserstein Distance Chen, Zijian Das, Soumya Chung, Moo K. Neurons and Cognition Image and Video Processing We present the unified computational framework for modeling the sulcal patterns of human brain obtained from the magnetic resonance images. The Wasserstein distance is used to align the sulcal patterns nonlinearly. These patterns are topologically different across subjects making the pattern matching a challenge. We work out the mathematical details and develop the gradient descent algorithms for estimating the deformation field. We further quantify the image registration performance. This method is applied in identifying the differences between male and female sulcal patterns. |
| title | Sulcal Pattern Matching with the Wasserstein Distance |
| topic | Neurons and Cognition Image and Video Processing |
| url | https://arxiv.org/abs/2307.00385 |