基于免疫克隆选择算法的图像分割

图像分割是图像处理领域中不可缺少的一个分支。该文基于阈值分割方法，将免疫克隆选择优化算法应用到图像分割中，提出了一种新的图像分割算法。详细叙述了算法机理，并对算法复杂度进行了理论分析以及实验数据比较。在仿真实验中，将遗传算法和免疫克隆选择算法分别独立运行10次，对10次得到的阈值以及均值、方差进行了比较，并将函数评价次数作为算法复杂度的评价指标。该文算法不仅能够对图像进行准确的分割，而且在同样的种群规模下能够以较少的迭代代数和较低的函数评价次数得到最优阈值。仿真结果表明，该方法应用在图像分割中是可行的、有效的。

Application of Immune Clone Selection Algorithm to Image Segmentation

Image segmentation is a significant part in image processing field. Inspired by the threshold-based segmentation methods, a novel algorithm based on immune clone selection and optimal entropy theory is presented in this paper. Immune clone selection algorithm performs not only local but also global search, and has better performance than Genetic Algorithm(GA) in searching for the optimal entropy threshold of images. The algorithm is depicted in detail and the computational complexity is given. In experiments, natural image and SAR image are selected, and the algorithm runs ten times independently and the mean numbers of function values are presented as the evaluation of the algorithm complexity. It shows that the algorithm presented in this paper can find better solutions with small generation and mean numbers of function values. So this method has better performance in stabilization and convergence than GA. Experimental results show that this method is feasible and effective.