基于局部与全局信息的活动轮廓图像分割模型

根据贝叶斯分类准则提出了一种改进的基于局部与全局信息的水平集图像分割模型。首先,利用图像的局部信息建立了局部能量项,引导目标附近的演化曲线停在目标边缘上;然后,利用图像的全局信息建立了全局能量项,加速远离目标边缘处演化曲线的演化;最后,提出了一种联合局部能量项和全局能量项的统一的水平集模型架构,提高了分割效率和分割灰度不均匀图像的能力。分割实验结果表明，该改进模型不但提高了对初始轮廓位置的鲁棒性,而且在分割灰度不均匀的图像时也取得了令人满意的分割结果。

Active contour model based on local and global information for image segmentation

According to Bayesian classification criteria, an improved level set method for image segmentation based on local and global information is proposed. Firstly, a local energy term based on local intensity information is defined. It can guide the evolving curve near the target settled on the boundaries. Secondly, a global energy term is built according to the global intensity information, so as to accelerate the evolution of the evolving curve far away from the target. Finally, a unified level set framework is proposed which combines the local energy term and global energy term together to improve the efficiency of segmentation and deal with images with intensity inhomogeneity. Experimental results show that this model is robust to the position of initial contour. In addition, it can obtain prod satisfying results in segmenting images with intensity inhomogeneity.