@Article{cmes.2023.030915,
AUTHOR = {Shupeng Qiu, Chujin Lin, Wei Zhao},
TITLE = {An Efficient Local Radial Basis Function Method for Image Segmentation Based on the Chan–Vese Model},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {139},
YEAR = {2024},
NUMBER = {1},
PAGES = {1119--1134},
URL = {http://www.techscience.com/CMES/v139n1/55106},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2023.030915}
}