关键词: 图像分割, 含噪图像, 非下采样Contourlet变换, 改进PSO算法

Abstract: In order to improve the segmentation effect and speed up the segmentation procedure of noise images, this paper proposes an efficient image segmentation method, i.e. IPSOC, which combines Nonsubsampled Contourlet Transform（NSCT） with Particle Swarm Optimization（PSO） algorithm. In this method, an original image firstly is decomposed with multi-scale NSCT transform. Then low frequency coefficients at the highest level are used to reconstruct an approximate image, and after the two-dimensional histogram of the reconstructed image and its mean-filtered image are produced, its trace of the between-class scatter matrix is taken as the object function for searching the best threshold. Simultaneously, an improved PSO algorithm is selected as the parallel scheme, which makes some progress compared to the standard PSO, such as real-time updating the individual and the global optimal information, preventing the stagnation of particles, and regarding threshold-kept times as one of the termination conditions. Experimental results show that IPSOC obviously improves both segmentation speed and segmentation quality, compared with some methods based on genetic algorithm and artificial fish algorithm.

Key words: image segmentation, noise image, Nonsubsampled Contourlet Transform（NSTC）, improved PSO algorithm