带空间约束的邻域中值加权FCM图像分割算法

摘要

Euclidean distance is the most commonly used distance measurement method in the process of clustering analysis.The traditional Euclidean distance image segmentation method does not consider the spatial information,neighborhood characteristics and other factors.In order to use more image space information to improve the quality of image segmentation,in addition to implanting spatial constraints information of pixels,we propose an alternative neighborhood median weighted Euclidean distance to replace the Euclidean distance.The results of segmentation experiments on multiple images show that,compared with the existing algorithms,this algorithm cannot only improve image segmentation effect with a better noise resistance,but also accelerate the convergence and obtain high efficiency.%在聚类分析过程中,欧氏距离是最为常用的距离度量方法,而传统的基于欧氏距离的图像分割方法没有综合考虑空间信息和邻域特征等因素.提出了一种用邻域中值加权欧氏距离替代欧氏距离的度量方法,同时植入像素空间约束信息,这样可以利用更多的图像空间信息来改善图像分割质量.通过对多幅图像的分割实验结果表明,与已有的算法相比,本算法不仅能提升图像分割效果,具有更好的噪声抵抗性,同时能加速算法的收敛速度,从而提高了分割效率.