基于图像层的双水平集图像分割

传统C-V模型可以将待分割图像分割成目标和背景两区域,但无法实现对多目标图像的分割.多相C-V模型能够对多目标图像进行分割,但需要多次迭代,计算量较大.为了解决上述问题,提出一种基于图像层的双水平集分割算法,该算法通过引入背景填充技术来改变图像背景,从而形成新的图像层,双水平集不断地在新的图像层中进行分割,直到所有目标被分割.这样通过双水平集就可以实现对多目标图像的分割.实验结果表明:该算法能够实现多目标分割,且迭代次数较少,同时具有较强的抗干扰能力和较快的收敛速度.

Double Level Set Image Segmentation Based on Image Layer

Traditional C-V model can divide the image into object and background,but can not be achieved on the multi-objective image segmentation.Multiphase C-V model for image segmentation requires more iterations and more computing time.In order to slove these problems,this paper proposed a double level set image segmentation algorithm based on image layer.The algorithm introduces the background filling technology to change the image background,forming a new image layer,and the double level set continues division in the new image layer,until all objects are segmented.Through the new image layer,the double level set can achieve the multi-objective image segmentation.The experimental results show that the algorithm can realize multi-objective segmentation with less iteration,also has strong anti-interfe-rence ability and faster convergence speed.