Open Access
ARTICLE
An Efficient Local Radial Basis Function Method for Image Segmentation Based on the Chan–Vese Model
Shupeng Qiu, Chujin Lin, Wei Zhao*
School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, 510520, China
* Corresponding Author: Wei Zhao. Email:
(This article belongs to the Special Issue: New Trends on Meshless Method and Numerical Analysis)
Computer Modeling in Engineering & Sciences 2024, 139(1), 1119-1134. https://doi.org/10.32604/cmes.2023.030915
Received 02 May 2023; Accepted 28 July 2023; Issue published 30 December 2023
Abstract
In this paper, we consider the Chan–Vese (C-V) model for image segmentation and obtain its numerical solution accurately and efficiently. For this purpose, we present a local radial basis function method based on a Gaussian kernel (GA-LRBF) for spatial discretization. Compared to the standard radial basis function method, this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain. Additionally, since the Gaussian function has the property of dimensional separation, the GA-LRBF method is suitable for dealing with isotropic images. Finally, a numerical scheme that couples GA-LRBF with the fourth-order Runge–Kutta method is applied to the C-V model, and a comparison of some numerical results demonstrates that this scheme achieves much more reliable image segmentation.
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APA Style
Qiu, S., Lin, C., Zhao, W. (2024). An efficient local radial basis function method for image segmentation based on the chan–vese model. Computer Modeling in Engineering & Sciences, 139(1), 1119-1134. https://doi.org/10.32604/cmes.2023.030915
Vancouver Style
Qiu S, Lin C, Zhao W. An efficient local radial basis function method for image segmentation based on the chan–vese model. Comput Model Eng Sci. 2024;139(1):1119-1134 https://doi.org/10.32604/cmes.2023.030915
IEEE Style
S. Qiu, C. Lin, and W. Zhao "An Efficient Local Radial Basis Function Method for Image Segmentation Based on the Chan–Vese Model," Comput. Model. Eng. Sci., vol. 139, no. 1, pp. 1119-1134. 2024. https://doi.org/10.32604/cmes.2023.030915