Image segmentation is an essential analysis tool in the field of computer vision, and the level set method has been widely used in image segmentation. Specifically, the edge-based level set models can reduce many undesired regions because they mainly rely on the edge information. However, the edge-based level set models are usually sensitive to the initial condition, which limits their application. To overcome this shortcoming, a global-to-local region-based indicator is designed in this paper, which is utilized to embed the region information into the edge-based models. Unlike the edge-based indicator frequently used in the edge-based models, the proposed region-based indicator can allow bidirectional motion of the active contour curve according to the region information. In general, the proposed region-based indicator can intrinsically incorporate the edge information and region information into one single energy function. Experimental results on synthetic images, natural images and medical images validate the effectiveness of the proposed method. Compared with some other level set models, the proposed method generally achieves better performance.

图像分割是计算机视觉领域中必不可少的分析工具，而水平集方法已被广泛应用于图像分割中。具体地，基于边缘的水平集模型可以减少许多不期望的区域，因为它们主要依赖于边缘信息。然而，基于边缘的水平集模型通常对初始条件敏感，这限制了它们的应用。为了克服这一缺点，本文设计了一种基于全局到局部区域的指标，该指标用于将区域信息嵌入到基于边缘的模型中。与基于边缘的模型中经常使用的基于边缘的指示器不同，所提出的基于区域的指示器可以根据区域信息允许活动轮廓线的双向运动。一般来说，所提出的基于区域的指示符可以固有地将边缘信息和区域信息合并到一个单一的能量函数中。在合成图像，自然图像和医学图像上的实验结果验证了该方法的有效性。与其他一些水平集模型相比，该方法总体上具有较好的性能。