基于变动权值的混合水平集图像分割模型

Hybrid Level Set Image Segmentation Model Based on Variable Weights

基于局部区域的活动轮廓模型(LRAC)分割图像时对初始轮廓的严重依赖性,提出一种基于局部和全局区域结合的水平集图像分割算法。结合Chan-Vese水平集模型和LRAC模型的特点,在构造水平集函数时定义了变动的权值参数,将水平集函数的局部和全局能量泛函项结合起来,其中,权重参数由图像梯度和图像演化曲线内外局部均值定义。另外,在水平集函数演化时采用窄带法,以减小计算的时间复杂度。实验结果表明,该算法模型兼有CV模型和LRAC模型的优点,比LRAC模型对初始轮廓选取的依赖性低,收敛速度快;比窄带CV模型的对目标边缘分割效果好。

In this paper, a new algorithm of image segmentation based on the combination of the local region and global region was proposed to solve the dependence of the initial contour on localizing region-based active contour （LRAC） model. The algorithm combine the characteristics of Chan-Vese model and LRAC model. When constructing the level set function, the variable weight parameters were defined to combine the local and global energy functional terms of the level set function. Further, the weight parameters were defined by image gradient and the mean of the inner and outer pixels at the local image. In addition, the narrow band method was used in the evolution of the level-set function to reduce the complexity of computation time. Experimental results show that our model has the advantages of both CV model and LRAC model. Compared with LRAC model, the method we proposed relies much less on the initial contour and has a better convergence rate. While compared with CV model, the precision of our model is higher in the effect of target edge segmentation.