中文摘要：

基于边的水平集合模型不与弱边为图象给令人满意的结果，并且基于区域的模型为紧张不同类图象糟糕表演。在这份报纸，我们建议两个都集成坡度信息和区域信息的一个改进基于区域的水平集合模型。建议模型定义一个新奇外部精力术语，它由坡度信息和签署的压力力量功能组成。为了消除传统的水平集合模型，的重新初始化过程，一个内部精力术语也被介绍让水平集合功能维持签署的距离功能。与传统的模型相比，我们的模型对有弱边和紧张不同类的图象是更柔韧的。从腹的 CT 图象的肝分割的实验表明建议方法的有效性和精确性。

英文摘要：

The edge-based level set model gives no satisfactory results for images with weak edge, and the region-based model performs poorly for intensity inhomogeneity images. In this paper, we propose an improved region-based level set model that integrates both the gradient information and the region information. The proposed model defines a novel external energy term, which consists of gradient information and signed pressure forces function. In order to eliminate the re-initialization procedure of traditional level set model, an internal energy term is also introduced for the level set function to maintain signed distance function. Compared with traditional models, our model is more robust against images with weak edge and intensity inhomogeneity. Experiments on liver segmentation from abdominal CT images demonstrate the effectiveness and accuracy of the proposed method.