A Kind of Two-Dimensional Entropic Image Segmentation Method Based on Artificial Immune Algorithm

Two-dimensional entropic segmentation method has been greatly developed because of high segmentation accuracy and good stability, while a hard problem is that it gives rise to the exponential increment of computational time in comparison with the traditional one-dimensional histogram partition technology. To solve this problem, a new kind of image thresholding method is presented based on the combination of the artificial immune algorithm (AIA) and two-dimensional entropy techniques in this paper. This method can effectively improve the computation time and avoid getting into local optimization of the threshold by making use of AIA´s characteristics of the intelligent computation, adaptive evolution and globally optimizing. The test results show that the method is effective and practicable