The level set method is widely used in medical image segmentation, in which the performance is seriously subject to the initialization and parameters configuration. An automatic segmentation method was proposed in this paper, which integrates fuzzy clustering with level set method through a dynamic constrained term in the new energy functional. It is able to use the results of fuzzy clustering directly, which can control the level set evolution. Moreover, the added constrained term is changing continuously until getting the final results. Such algorithm eliminates the manual operation a lot and leads to more robust segmentation results. With the split Bregman method, the minimization of the new energy functional is fast. The proposed algorithm was tested on some medical images and also compared with other level set models and the state-of-the-art method such as U-Net. The quantitative and qualitative experimental results show its effectiveness and obvious improvement for medical image segmentation.

水平集方法广泛应用于医学图像分割中，其性能严重受初始化和参数配置的影响。提出了一种自动分割方法，该方法通过动态约束项将模糊聚类与水平集方法相结合，实现了新能源功能的发展。它能够直接使用模糊聚类的结果，从而可以控制水平集的演化。此外，增加的约束项会不断变化，直到获得最终结果。这样的算法大大减少了手动操作，并导致更鲁棒的分割结果。使用分裂的Bregman方法，新能源功能的最小化很快。该算法在一些医学图像上进行了测试，并与其他水平集模型和最新方法（例如U-Net）进行了比较。定量和定性的实验结果表明了其在医学图像分割中的有效性和明显的改进。