TY  - EJOU
AU  - Qiu, Shupeng 
AU  - Lin, Chujin 
AU  - Zhao, Wei 

TI  - An Efficient Local Radial Basis Function Method for Image Segmentation Based on the Chan–Vese Model
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 139
IS  - 1
SN  - 1526-1506

AB  - 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.
KW  - Image segmentation; Chan–Vese model; local radial basis function method; Gaussian kernel; Runge–Kutta method

DO  - 10.32604/cmes.2023.030915