Research On Image Segmentation Method Based On Improved Regional Level Set

With the development of information technology,the human society has produced a large amount of image information.Mining,processing and analyzing the rich information contained in the image has important guiding significance for the production and life of human beings.As the premise of image analysis and understanding,image segmentation has always been the key and difficult problem in the field of image processing.There are many kinds of methods for image segmentation,and the image segmentation based on the idea of level set has become a widely concerned method due to its advantages such as flexible topological structure fransformation and strong mathematical foundation.In this paper,image segmentation method based on regional level set are deeply analyzed and studyed,Then,an improved segmentation model is proposed to solve the problems of this method that is sensitive to the initial contour and the noise.Finally,the improved model with several classical models for segmentation experiments of images with intensity inhomogeneity,images with moise and natural image to verify the superiority of the improved model.The main work of this paper is as follows(1)A level set image segmentation method combining global information and local fitting information is proposed.Aiming at the problem that the local information-based LATE model is sensitive to the initial contour and noise,which results in the segmentation result falling into the local extremum,the C V model based on the global intensity information is introduced as the global energy term,the local information-based LATE model is taken as the local energy term,and the two models anr combined by linear rules to form the fidelity term of the segmentation model Meanwhile,the penalty term is introduced to ensure that the level set function remains a symbolic distance function in the evolution process,so as to improve the stability of numerical calculation.The introduction of length term is helpful to smooth the evolution curve.The comparison of several segmentation indexes shows that the improved segmentation model can improve the robustness of the LATE model to the initial contour and noise,as well as the accuracy of segmentation(2)A level set image segmentation method based on fusion of glob al information and local Gauss statistics is proposed.In order to deeply mine the grayscale change information in the image and achive the purpose of accurate segmentation,this paper uses multidimensional Gauss probability distrubution function to simulate the gray level distribution of local area.According to the theory of statistical probability,the Gaussian probability distribution function can be constructed as a logarithmic likelihood estimation function,and the likelihood function is regarded as a local energy term that includes local mean and square.By using these two kinds of Gaussian statistics to fit the gray information of the local region,the segmentation is able to be finished.In addition,the introduction of CV model based on global information is helpful to reduce the sensitivity of segmentation model to initial contour and noise.Finally,the validity of the model is verified by a number of comparative experiments.