一种改进遗传退火算法的图像分割方法  

An Improved GASA Algorithm for Image Segmentation

作　　者：谭定英[1] 陈平平[1] 李学征[1] 唐蓉[1] 

机构地区：[1]广州中医药大学医学信息工程学院,广东广州510006

出　　处：《计算机与现代化》2014年第7期80-84,88,共6页Computer and Modernization

摘　　要：图像分割是图像处理和分析的基础,本文通过分析遗传算法(Genetic Algorithm,GA)在图像分割中的应用优劣,提出利用模拟退火思想的改进遗传退火(Genetic Simulated Annealing Algorithm,GASA)的图像阈值分割算法,算法整个运行过程由冷却温度进度表控制,使用改进的最大类间方差公式作为遗传算法的适应度函数,从而求得灰度图像的一个最佳阈值用于图像分割。实验结果表明,基于改进遗传退火算法的最大类间方差图像分割方法能较好提高算法的全局搜索能力,避免遗传算法陷入局部最优,并且能更快速、更稳定收敛到最佳的分割阈值,得到更好的图像分割效果。Image segmentation is the foundation of the image processing and analysis .The Otsu segmentation algorithm and genet-ic algorithm are analyzed in this paper , in order to improve the running performance of the algorithm , simulated annealing is intro-duced to put forward a kind of improved genetic simulated annealing algorithm （ GASA） .The whole running process of this algo-rithm was controlled by the temperature cooling schedule , with the improved Otsu method being used as the fitness function of the genetic algorithm .After several rounds of computing , an optimal threshold value was obtained for image segmentation .The exper-iments’ results showed that the image segmentation based on the GASA could be good at enhancing the comprehensive search a -bility of the algorithm, and avoiding the genetic algorithm’s falling into local optimization.Meantime, it would not only converge to the optimum segmentation threshold faster and more steadily , but also obtain higher segmentation quality .

关 键 词：图像分割 遗传算法 遗传退火算法 最大类间方差 阈值 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]