【摘要】 针对传统FCM图像分割算法没有充分利用像素点的邻域关系与局部信息,导致算法对噪声敏感,不能准确地分割出弱边缘区域的问题,提出一种结合图像全局信息与边缘局部信息的分割算法.首先引入局部窗口变异系数和邻域灰度相似性2个概念重新设计模糊因子,使其能够更精确地衡量邻域点对中心点的影响程度,降低噪声对分割的影响;然后在分割结果的边缘上选取局部窗口,将边缘局部信息融入分割过程;最后在选取窗口中再分割,等同于在边缘处增加多个更符合局部信息的聚类中心来纠正被错误分类的像素点.实验结果表明,该算法能够有效地消除噪声对分割的影响,同时保留更多图像细节信息.更多还原

【Abstract】 The standard fuzzy c-means(FCM)for image segmentation is very sensitive to noise and cannot get weak boundary accurately due to its insufficient usage of local information.To overcome these drawbacks,we present a novel image segmentation algorithm called GLFCM by combining local information on edge with FCM algorithm.First,the algorithm designs a new fuzzy factor integrating coefficient of variation of local windows and pixel gray value similarity,to control the trade-off between smoothing and clustering more accurately and enhance the robustness.Then,local windows near boundaries are selected and local information is utilized to optimize the pixels near the boundaries.Finally,we segment the selected windows,to improve the incorrect parts near the boundaries of the clusters.Experimental results show that the proposed algorithm has the stronger anti noisy property and higher segmentation accuracy.更多还原