A novel method for image segmentation using reaction-diffusion model

We propose an image segmentation model that is derived from reaction-diffusion equations and level set methods. In our model, a diffusion term is used for regularization of a level set function, and a reaction term has the desired sign property to force the level set function to move up or down and finally identify an object and its background. Our level set function can be initialized to any bounded function (e.g., a constant function). The proposed model can be applied to a wider range of images with promising results, especially for real images that have high noise and blurred boundaries. This study gives a new method for the further investigations of reaction-diffusion equations directly for segmentation.