基于区域进化的区域增长图像分割

为克服经典区域增长算法中门限选择困难、分割稳定性不高与串行处理速度慢的不足,提出了基于区域进化的快速区域增长图像分割算法。引入了新的区域能量表示模型,并给出了迭代进化形式。在区域增长过程中,逐渐增加区域增长的门限,通过对能量函数的动态优化来逼近最佳分割结果。仿真实验表明,该方法能有效地避免经典区域增长算法中门限选择的困难,采用区间连通处理技术代替单一像素串行迭代处理方式,可使分割速度提高十多倍。

Fast region growing optimal image segmentation method based on region evolution

To overcome the difficulty of manual threshold selection and slow speed of conventional region growing image segmentation algorithm,a region evolution image segmentation algorithm is proposed.A novel region energy model and the corresponding iterative evolution steps are given.In region growing processing step,the threshold is increased gradually,so the optimal segmentation results are obtained by dynamic optimizing the energy model.Comparative experimental results show that this algorithm performs better obviously in automatic optimal threshold selection,processing speed and segmentation veracity than conventional region growing algorithm.