Abstract: To effectively accomplish analysis tasks for noisy images, two new algorithms for dynamic image segmentation based on 2-D genetic algorithms are proposed. In these new algorithms: (a) coding with 2-D chromosome by taking respectively the surface of thresholds and surface of fuzzy membership is adopted; (b) initialization of population with global thresholding methods and fuzzy set theory is exp rienced; (c) construction of fitness function by using energy function of Hopfield neural network as well as by integrating objective function of FCM algorithm, thresholding algorithms and general criteria of image segmentation is employed. A number of experiments using real images are performed, in which the proposed algorithms are compared with some typical segmentation algorithms. Results show that the new algorithms have superior performance for noisy images.