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Due to the problem that local binary fitting model is easy to fall into local extremum, based on the hypothesis that the brightness of the image on both sides of the target boundary has contrast, an improved method of adding contrast constraints to the fitting function is proposed in this paper. The contrast-constrained model can effectively overcome the problem that energy is easily trapped in local extremum, but there is still a problem that requires the initialization curve to be close to the edge of the target. Therefore, balloon force is added to the contrast-constrained model, which enlarges the scope of external force and enhances the robustness of the model to initialization. Meanwhile, the diffusion filtering method with adaptive image structure is introduced into image registration, and the anisotropic diffusion function with the ability of feature preservation and consistency enhancement is defined as the regularization term of the model. The improved model can effectively maintain image features and achieve accurate registration of complex images. In this paper, brain image (MRI) in Mcgill database is taken as an example to verify the simulation results. The simulation results verify the reliability and validity of the partial differential equation-based algorithm.

Application of Partial Differential Equation in Brain Image Segmentation and Registration