基于图像层的双水平集图像分割

Double Level Set Image Segmentation Based on Image Layer

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。

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 compu- ting 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.