Guo等人利用n个水平集方程构造n个区域提出一种改进的CV模型（简称MCV模型），该模型需要的迭代次数很少，提高了图像分割的效率，但其分割结果受初始曲线位置的影响较大，极易陷入局部最优，无法分割复杂图像，且利用传统的Heviside函数无法得到准确的均值信息，因此无法保证数值的稳定性。本文对MCV模型进行改进，先对图像进行预分割得到初始曲线以提高分割效率且能保证分割结果全局最优，构造新的符号函数取代传统的Heviside函数改进MCV模型以保证数值稳定性。对MR图像进行的分割实验表明，其在保证迭代次数较少

Guo proposed an improved CV model （MCV model） that needs less iteration, but trapped in local optima for the influence of regions of initial contours； also some points were segmented in a wrong region or were omitted. The means gated by traditional Heviside function isnt accurate to keep the numerical stability. In this article we modify the MCV model, propose a new model using n equations of level set to structure n Regions： pre-segment the image to get the initial contours to avoid the results trapping in local optima and improve the efficiency of segmentation.Then we modify the MCV model by structuring a new symbol function to replace the Heviside function which can keep the numerical stability. Experiment results show that the new model can obtain good results efficiently.