As active contours without edges (C-V) model is difficult to segment thyroid ultrasound image with intensity inhomogeneity. Therefore, the improved C-V ultrasound image segmentation model of combining local information is proposed. First, the local information is not affected by the gray distribution. So, through this characteristic, we constructed a new speed function by using local image fitting information. According to the change of local gray level, the speed function can flexibly control curve evolution rate. Then, the speed function was incorporated into the C-V model, and had the ability of global segmentation. The experiment results demonstrate that the proposed model can achieve accurate segmentation for thyroid tumor ultrasound image with intensity inhomogeneity. And the segmentation efficiency is also improved.%针对无边缘主动轮廓模型(Active contours without edges, C-V)难以分割灰度分布不均匀的甲状腺超声图像，本文提出结合局部信息改进的C-V超声图像分割模型。该方法根据局部信息具有不受灰度分布影响的拟合特性，利用图像局部拟合信息构造一种新的速度函数，使速度函数依据图像局部灰度变化控制曲线的演化速率；然后将该速度函数引入到C-V模型中，具有全局分割能力。实验结果表明，本文方法可以实现对灰度分布不均匀的甲状腺肿瘤超声图像的准确分割，且分割效率也有所提高。
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