【摘要】 模糊C均值算法用于图像聚类时,仅考虑图像的灰度信息,忽略灰度的空间分布,未充分利用分割前后图像间的关系。从分割后图像的类距离出发,并利用聚类分割前后图像间的互信息,以基于对称分布多样性的粒子群算法为优化技术,构造了一种新的图像分割方法——基于互信息和类距离测度最优的图像聚类算法。对医学图像进行仿真,实验结果表明该算法得到的图像边界清晰连续,图像的内部特征保持完好,与多种聚类算法相比,图像分割的质量明显得到提高。更多还原

【Abstract】 When Fuzzy C-Means(FCM) is utilized for image clustering,only the gray level information is considered,ignoring the spatial distribution,so the connection between the original and segmented image is not fully used.This paper constructs a new image clustering segmentation method,which is based on the mutual information between the original and segmented image and cluster distance measure optimum of segmented image.The Particle Swarm Optimization(PSO) based on diversity of symmetrical distribution is adopted as optimization method.Simulations with the medical images by the new method reveal that the results have clear and continuous image boundary,keep complete internal image characteristics.Experimental results show that the image segmentation quality is improved obviously.更多还原