Abstract
The Chan-Vese model using variational level set method (VSLM) has been widely used in image segmentation, but its efficiency is a challenge problem due to high computation costs of curvature as well as the Eiknal equation constraint. In this paper, we propose a continuous Max-Flow (CMF) method based on discrete graph cut approach to solve the VSLM for image segmentation. Firstly, we recast the original Chan-Vese model to a continuous max-flow problem via the primal-dual method and solve it using the alternating direction method of multipliers (ADMM). Then, we use the projection method to recover the continuous level set function for image segmentation expressed as a signed distance function. Finally, some numerical examples are presented to demonstrate the efficiency and accuracy of the proposed method.
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Acknowledgments
The work has been partially supported by China Postdoctoral Science Foundation (2017M612204, 2015M571993), and the National Natural Science Foundation of China (61602269). Authors thank Prof. Xue-Cheng Tai, Department of Mathematics at University of Bergen, Prof. Xianfeng David Gu, Department of Computer Science, State University of New York at Stony Brook for their instructions and discussions.
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Hou, G., Pan, H., Zhao, R., Hao, Z., Liu, W. (2018). Image Segmentation via the Continuous Max-Flow Method Based on Chan-Vese Model. In: Wang, Y., et al. Advances in Image and Graphics Technologies. IGTA 2017. Communications in Computer and Information Science, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-10-7389-2_23
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DOI: https://doi.org/10.1007/978-981-10-7389-2_23
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