Parameter Selection of Generalized Fuzzy Entropy-Based Thresholding Segmentation Method with Particle Swarm Optimization

Image thresholding method based on generalized fuzzy entropy segments the image using the principle that the membership degree of the threshold point is equal to m (0<m<1), better segmentation result can be obtained than that of traditional fuzzy entropy method, especially for images with bad illumination. The main problem of this method is how to determine the parameter m effectively. In this paper, we use particle swarm optimization to solve it. Based on an image segmentation quality evaluation criterion and the maximum fuzzy entropy criterion, using particle swarm optimization, the optimal parameter m and the membership function parameters (a, b, d) is automatically determined respectively, realizing the aim of automatic selection the threshold in generalized fuzzy entropy-based image segmentation method. Experiment results show that our method can obtain better segmentation results than that of traditional fuzzy entropy based method.