Noise Image Segmentation Using Fisher Criterion and Regularization Level Set Method

With the active contour model gradually coming to maturity and development, how to improve noise immunity of the model has become an important research topic. Considering the image segmentation from the perspective of the classification, Fisher criterion function is introduced into image segmentation creatively, which uses Fisher criterion to guide the image segmentation and gives Fisher explanation about the C-V model. For the noise image, non-negative robust function is defined as edge-preserving potential function and ensures that the edges are not blurred. In order not to smooth out the edge and texture information in the process of removing noise, we introduce a robust function for regularization, regard Fisher criterion as the standard of image segmentation, and establish a variational level set model which is the combination of region-based model and edge-based model. The model can perform simultaneously denoising and segmentation. The numerical solution of the model is discussed in detail. Experiments confirm that the theory is correct and reliable, and the regularization term constraints are able to achieve good effect in the respect of noise removal and edge preservation during the process of image processing. The model for image denoising and segmentation is more efficient than that using clustering algorithm, relaxation iterative algorithm and mean shift algorithm with three sets of experiments.