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.