基于并行遗传算法的Otsu双阈值医学图像分割

传统遗传算法用于搜索某些函数极值时精确度较低且稳定性较差。针对该问题，提出了一种基于并行遗传算法的Otsu双阈值医学图像分割算法。在该算法中，进化在多个不同的子群中并行进行，避免单种群进化过程中出现的过早收敛现象，提高整个算法的收敛速度。100 次阈值计算实验结果表明，提出的分割算法与传统遗传算法相比，不仅能够对图像进行准确的分割，而且具有更强的精确性和稳定性。其收敛速度明显优于基于单种群的遗传算法的Otsu双阈值医学图像分割。

An Otsu Dual-threshold Value Method Based on Parallel Genetic Algorithmfor Medical Image Segmentation

Medical Image Segmentation is a hot topic in the community of medical images analysis. The traditional genetic algorithm is sometimes inaccurate and instable when it is used in searching the best solutions of some functions. To solve the problem, an Otsu Dual-threshold Value Method based on parallel genetic algorithm for Medical Image Segmentation is proposed. In the algorithm, evolution is performed among different subgroups in parallel. The avoidance of premature convergence of single-species evolutionary process improves the convergence efficiency of the algorithm. The thresholds searching results for 100 times show that the algorithm presented in this paper can not only find better solutions, but also be more stable and accurate than the traditional genetic algorithm. Its convergence is improved more quickly than that of the single-species genetic algorithm.