结合显著性和边缘信息的水平集图像分割方法

Level set image segmentation method combining saliency and edge information

针对LBF模型对初始轮廓的依赖性和对边缘的弱控制能力,研究了一种结合显著性和边缘信息的水平集图像分割方法。首先,结合小波分析理论,基于视觉注意机制构造图像显著图;然后,利用小波分解所描述的图像边缘信息,构造边缘检测函数,同自适应初始轮廓一起引入到LBF水平集模型中,并用有限差分法进行数值求解。实验结果表明,提出的图像分割方法能有效降低初始轮廓位置对活动轮廓模型的影响,对合成图像、自然图像均有较好的分割结果,相较于其他传统方法具有更高的演化效率和分割质量。

To address the LBF model’s dependence on the initial contour and the weak control over the edge,we investigated a method of the level set image segmentation combining saliency and edge information.First,we generated the image saliency map combined with the theory of wavelet analysis and based on the visual attention mechanism.Then,the edge detection function was constructed using wavelet decomposition information.It was introduced into the improved LBF level set model together with the adaptive initial contour,and the finite difference method was used for numerical solution.The experimental results show that the image segmentation method proposed in this paper can effectively reduce the influence of the initial contour position on the active contour model,and can yield better segmentation results for both synthetic images and natural images.Compared with other traditional methods,it is of higher evolution efficiency and segmentation quality.