Sulcal Pattern Matching with the Wasserstein Distance
Neurons and Cognition
2026-04-24 v2 Image and Video Processing
Abstract
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.
Cite
@article{arxiv.2307.00385,
title = {Sulcal Pattern Matching with the Wasserstein Distance},
author = {Zijian Chen and Soumya Das and Moo K. Chung},
journal= {arXiv preprint arXiv:2307.00385},
year = {2026}
}
Comments
Published in Proceedings of the 2023 IEEE International Symposium on Biomedical Imaging (ISBI)