基于贝塞尔滤波的水平集正则化图像分割方法

针对水平集函数在演化过程中的初始化敏感和数值稳定性问题,提出了一种新的基于贝塞尔滤波的正则化方法,并将其嵌入到经典的可变区域拟合(Region-Scalable Fitting,RSF)模型中,从而构成新的能量模型。首先,利用K均值算法进行自动初始化,再加以修正生成标准的初始水平集函数,以解决RSF模型对初始化敏感的问题；其次,利用RSF模型自身优点对图像进行迭代分割,同时在迭代过程中利用提出的方法对水平集函数进行正则化处理,保持迭代过程中的稳定性；最后,实现精确的分割效果。实验结果表明,提出的正则化方法有效地保持了水平集函数的稳定性。将新的模型与多种基于区域的模型进行对比,仿真实验表明,提出的方法具有较高的算法效率与分割精度。

Image Segmentation Method of Level Set Regularization Based on Bessel Filter

A new regularization method based on Bessel filter was proposed to solve the problem of numerical stability of the level set function in the evolutionary process.A new energy model was constructed by embedding this method into the classical region-scalable fitting(RSF) model.Firstly,the K-means algorithm is used to generate the initial level set function automatically to solve the problem of the initialization sensitivity of the RSF model.Secondly,the advantages of region-scalable fitting model are used for iterative segmentation.Finally,in the iterative process,the proposed method is used to maintain the stability of the level set function in order to achieve accurate segmentation results.The experimental results show that the proposed regularization method effectively preserves the stability of the level set functions.The new model has higher efficiency and segmentation accuracy compared with other models based on region.