Research On Selective Segmentation Method Based On Active Contour Model

Image segmentation based on partial differential equations is currently a research hotspot.Because of its rigorous mathematical foundation and high-efficiency numerical scheme,this method becomes one of the most widely used algorithms in the field of computer vision.The active contour model based on the level set method is a typical method in the field of image segmentation based on partial differential equations.The basic idea is: First,the evolution curve is represented by a high dimensional embedding function,and the embedding function is introduced into the energy functional.Then the partial differential equations about the embedding function are obtained by the variational method.The final evolution result is the border of the target.Based on partial differential equation image segmentation method and level set method,this paper proposes two improved selective segmentation models for disadvantages of the existing models.These new selection segmentation models are applied to segmenting grayscale images.And then this paper extends the new selective segmentation model to texture image selective segmentation,which makes application of the segmentation model more widely.The main work of this paper is as follows:1.For the selective segmentation model based on edge information or global information cannot segment the intensity inhomogeneity and strong noise images effectively,the local statistical information of the original image and differential image is added to the selective segmentation model to overcome this problem.This proposed model utilizes local information of the original image to implement the selective segmentation of intensity inhomogeneity and the statistical information of the differential image to reduce the effect of noise on the segmentation.Compared with the existing selective segmentation models,the proposed model can accurately segment images with intensity inhomogeneity and strong noise.2.The selective segmentation model based on the local statistical information of the original image and the differential image is sensitive to the initial position of the evolution curve,especially for the image segmentation with weak texture and weak edges,it is easy to fall into a local optimum.Considering these shortcomings,this paper utilizes the fractional order differential mask operator to filter the original image,which can enhance the edges of the object and preserve the intensity and texture detail information.And then a fractional order differential fitting term is constructed and introduced into the selective segmentation model to obtain improved selective segmentation model based on fractional order differential.Compared with the former model,the segmentation results show that the model has better segmentation effect on weak and weak boundary images,and is more robust to the initial position of the evolution curve.3.Based on improved selective segmentation model,this paper extends it to texture image selective segmentation.Firstly,the texture features are described by the nonlinear structure tensor.Secondly,utilizing two eigenvalues of the nonlinear structure tensor,the edge stop function in the selective segmentation model is reconstructed.Then the average of the four feature channels contained in the nonlinear structure tensor is replaced the original image in the fitting term based on fractional order differential.The segmentation results show that the selective segmentation model based on structure tensor can achieve selective segmentation of texture images.