结合Voronoi划分HMRF模型的模糊ISODATA图像分割

Fuzzy ISODATA Image Segmentation Integrating Voronoi Tessellation HMRF Model

为了解决传统模糊聚类图像分割方法对噪声敏感及无法自动准确确定聚类数的问题,提出结合Voronoi划分HMRF模型的模糊ISODATA图像分割方法。利用Voronoi划分将图像域划分为若干子区域,以划分子区域为基本单元定义基于隐马尔科夫随机场(HMRF)模型的模糊聚类目标函数,以解决噪声敏感问题;通过迭代自组织数据分析技术算法(ISODATA)中聚类分裂、合并技术改变聚类数,以实现聚类数的自动确定。对模拟、合成图像和真实图像分割结果的定性、定量分析表明:提出算法不仅可以有效克服噪声和像素异常值对分割结果的影响,而且还能自动准确确定聚类数,实现自动变类图像分割。

In order to deal with the problem that the traditional fuzzy cluster segmentation algorithms were extremely sensitive to noise and could not determine the cluster number automatically, the algorithm of fuzzy ISODATA image segmentation in- tegrating Voronoi tessellation HMRF model is proposed. It divided the image domain into many sub-regions by Voronoi tessel- lation, and defined objective function with sub-regions based on Hidden Markov Random Field （HMRF） to reduce the effect of noise. Then, the cluster number was changed by Iterative Self-Organizing Data Analysis Techniques Algorithm （ISODA- TA） with cluster splitting and merging operations. Comparing the segmentation results of simulated, synthetic and real images from qualitative and quantitative analyses indicate that the proposed algorithm can not only overcome the effect of the image noises and outliers, but also obtain correct cluster number adaptively, and realize accurate image segmentation.