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| 基于并行遗传算法的Otsu双阈值医学图像分割 | |
| 引用本文： | 许良凤,林辉,罗珣,吴东升,李国丽,徐元英,景佳. 基于并行遗传算法的Otsu双阈值医学图像分割[J]. 工程图学学报, 2011, 32(2): 88-92 |
| 作者姓名： | 许良凤 林辉 罗珣 吴东升 李国丽 徐元英 景佳 |
| 作者单位： | 1. 合肥工业大学计算机与信息学院,安徽合肥,230009 2. 合肥工业大学应用物理系,安徽合肥,230009 3. 合肥工业大学科研处,安徽合肥,230009 4. 浙江工业大学信息工程学院,浙江杭州,310014 5. 合肥工业大学应用物理系,安徽合肥,230009;中国科学院等离子体物理研究所,安徽合肥,230031 |
| 基金项目： | 国家"973"计划资助项目，国家自然科学青年基金资助项目，国家自然科学基金资助项目，合肥工业大学校科学研究发展基金资助项目，合肥工业大学博士学位专项基金资助项目 |
| 摘 要： | 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。 |
| 关 键 词： | 医学图像 Otsu 阈值 遗传算法 |
An Otsu Dual-threshold Value Method Based on Parallel Genetic Algorithm for Medical Image Segmentation |
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| XU Liang-feng,LIN Hui,LUO Xun,WU Dong-sheng,LI Guo-li,XU Yuan-ying,JING Jia. An Otsu Dual-threshold Value Method Based on Parallel Genetic Algorithm for Medical Image Segmentation[J]. Journal of Engineering Graphics, 2011, 32(2): 88-92 | |
| Authors: | XU Liang-feng LIN Hui LUO Xun WU Dong-sheng LI Guo-li XU Yuan-ying JING Jia |
| Affiliation: | 1.School of Computer and Information,Hefei University of Technology,Hefei Anhui 230009,China;2.Application Physics Department,Hefei University of Technology,Hefei Anhui 230009,China;3.Research and Administration Department,Hefei University of Technology,Hefei Anhui 230009,China;4.School of Information Engineering,Zhejiang University of Technology,Hangzhou Zhejiang 310014,China; 5.Institute of Plasma Physics,Chinese Academy of Sciences,Hefei Anhui 230031,China) |
| Abstract: | 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. |
| Keywords: | medical image Otsu threshold genetic algorithm |
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