| 粒子群优化算法在脑部肿瘤图像分割中的应用 |
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| 引用本文： | 黄峰茜,陈春晓,吴文佳.粒子群优化算法在脑部肿瘤图像分割中的应用[J].河南科技大学学报(自然科学版),2007,28(6):97-99. |
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| 作者姓名： | 黄峰茜 陈春晓 吴文佳 |
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| 作者单位： | 南京航空航天大学,自动化学院,江苏,南京,210016;南京航空航天大学,自动化学院,江苏,南京,210016;南京航空航天大学,自动化学院,江苏,南京,210016 |
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| 摘 要： | 粒子群优化算法能选择适当的适应度函数,使每组粒子群根据相应的适应值搜索到最佳聚类中心,改善了FCM算法和K-means算法的不足,具有适应性强,实时性好,受噪声影响小等特点。本文将其应用于脑部肿瘤图像的分割,结果表明,粒子群聚类算法是一种很有潜力的图像分割方法。
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| 关 键 词： | 图像分割 粒子群优化 适应度函数 聚类 |
| 文章编号： | 1672-6871（2007）06-0097-03 |
| 修稿时间： | 2007-02-06 |
Application of PSO Algorithm to Brain Tumor Image Division |
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| HUANG Feng-Qian,CHEN Chun-Xiao,WU Wen-Jia.Application of PSO Algorithm to Brain Tumor Image Division[J].Journal of Henan University of Science & Technology:Natural Science,2007,28(6):97-99. |
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| Authors: | HUANG Feng-Qian CHEN Chun-Xiao WU Wen-Jia |
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| Abstract: | This paper uses PSO algorithm as a method on dividing up the brain tumor image.The key step of this algorithm is to define the proper fitness functions,which decides the fitness-values of the particles of each swarm.The fitness-values plays a quite important role in searching for the suitable cluster centers, which is applied to the image of brain tumor division.The PSO algorithm makes up some deficits of the FCM and K-means,with flexibility,real time,not sensitivity to the noise.The result shows that the PSO-cluster algorithm has much potential in image segmentation. |
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| Keywords: | Image segmentation Particle swarm optimization Fitness function Cluster |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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