利用具有图像增强能力的局部区域信息,定义一种新的符号压力函数(SPF)。用该SPF函数取代GAC模型中的边界停止函数,对GAC模型进行改进,提出一种新的区域活动轮廓模型,从而解决了非同质或弱边界图像的分割问题。继续采用Selective Binary and Gaussian Filtering水平集方法,避免水平集函数的重新初始化,简化新模型。真实图像和合成图像的实验结果表明,新模型与LBF模型具有相同的分割效果,但在计算效率上远优于LBF模型。新模型不仅能够分割非同质或弱边界图像,且具有亚像素分割精确性、抗噪性、局部全局选择分割性等性质。

By using the local regional information which has the ability to enhance the image, a new SPF function has been defined. The edge stopping function in the GAC model is replaced by the SPF function, and a new region-based active contour model is put forward by improving the GAC model. Therefore, images with intensity inhomogeneities and weak boundaries can be processed. The Selective Binary and Gaussian Filtering Level Set (SBGFRLS) method is continuously used in the new model which is simplified by avoiding the process of reinitializing the level set function. Experiments on real and synthetic images indicate that the new model has the same segmentation results as the LBF model, while the computational efficiencies improve significantly. The new model not only can segment images with intensity inhomogeneities and weak boundaries, but also has the properties such as sub-pixel accuracy, anti-noise nature, selective local or global segmentation, etc.