直觉模糊集人工鱼群搜索的人脑切片图像分解  

Decomposition of Brain Slice Image Based on Intuitive Fuzzy Sets of Artificial Fish Swarm Search

作　　者：王睿[1] 

机构地区：[1]四川职业技术学院计算机科学系,四川遂宁629000

出　　处：《科技通报》2014年第10期148-150,共3页Bulletin of Science and Technology

摘　　要：精密的大脑切片图像的微细分解处理是进行图像特征分析的基础,传统的人工鱼群算法对图像微细区域进行分解时,融入局部信息导致图像噪声增强,难以有效提取图像的数值特征信息,分解效果不好。提出一种基于直觉模糊集的人工鱼群搜索算法,根据模糊集理论,进行直觉模糊集构造。在人工鱼群寻优搜索到的引领粒子附近自组织搜索更优特征解,利用直觉模糊集的均匀遍历特性全局搜索微细特征,不需要人为的干预,更适合处理一些模糊的和不确定的问题,适用于图像的微细分解。仿真实验得出该算法在处理含强噪声的脑切片图像时,微细分解精度很好,精度和计算复杂度等方面较传统方法有优越性。The micro decomposition of the brain image is the foundation of image feature analysis. Traditional artificial fish swarm algorithm （AFSA） fuses the local information, which leads to image noise enhancement. It is difficult to effectively ex-tract the numerical image feature information, so the decomposition effect is not good. An improved micro decomposition method of slice image is proposed based on intuitive fuzzy sets of artificial fish swarm search, fuzzy set theory is used, and the intuitive fuzzy set is constructed. AFSA is used to search more feature, and get the self-organization search solution. The uniformly ergodic properties is used to search global micro characteristics, without the human intervention, so it is more suitable for dealing with fuzzy and uncertain problems. It is applicable to the image micro decomposition. The brain slices with strong noise is used as the sample in experiment, results show that the algorithm has better performance in preci-sion and computational complexity.

关 键 词：人工鱼群 图像分割 模糊集 微细特征 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]