Graph-based inhomogeneity image segmentation with the optimal transport metric

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Huang, Jisui and Chen, Ke and Alpers, Andreas and Lei, Na; (2026) Graph-based inhomogeneity image segmentation with the optimal transport metric. In: 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) . IEEE, Piscataway, NJ, pp. 6700-6707. ISBN 9798331515577 (https://doi.org/10.1109/bibm66473.2025.11357138)

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Abstract

Traditional variational models often fail to segment images in the presence of inhomogeneity or weak boundaries, partly due to their reliance on unreliable region metrics that quantify inhomogeneity based on a single mean value or a smoothed image serving as a mean function. The former, such as variance-based methods, are highly sensitive to image inhomogeneity, whereas the latter, such as local convolution-based approaches, lack a global receptive field. To address these issues, we employ an optimal transport-based data fidelity term in our segmentation objective functional. This term accounts for global differences between regions, resolving problems arising from local convolutions. It can also adaptively seek an optimized match between two probability density functions, proving more robust than relying solely on their mean values. Our proposed functional is minimized by gradually performing region merging. Experimental results demonstrate that our model outperforms state-of-the-art variational and deep learning models.

ORCID iDs

Huang, Jisui, Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623, Alpers, Andreas and Lei, Na;
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