Improvement of Grayscale Image Segmentation Based on PSO Algorithm

Image segmentation is the base of image 3D reconstruction. It is a critical step in image processing. Threshold segmentation is a simple and important method in grayscale image segmentation. Maximum entropy method is a common threshold segmentation method. This method only utilizes gray information. In order to adequately utilize spatial information of greyscale image, an improved 2D entropy segmentation method is proposed. This new method is called PSO-SDAIVE algorithm. In this new method, the computation of 2D entropy is improved. Otherwise, particle swarm optimization (PSO) algorithm is used to solve maximum of improved entropy. Maximum takes as the optimal image segmentation threshold. In this paper, two head CT images are segmented in experiment. Compare with other segmentation method. Experimental results show that this new method can quickly and accurately obtain segmentation threshold. Otherwise, this method has strong anti-noise capability and saves computation time.