Study On The Region-based Active Contour Model Of Image Segmentation And Its Application

Image segmentation is an important technology in image processing,and it is the basis of the image analysis and understanding.In recent decades,among many image segmentation methods,image segmentation methods based on partial differential equations have achieved great success.This paper focuses on the region-based active contour model based on the variational and level set method,and improve the defects of the traditional regional active contour model.The main contents are as follows:(1)Intensity inhomogeneity,strong noise,and blurred edges often occur in images.To solve such problems,this paper proposes an adaptive gaussian fitted energy model based on global and local intensity information.Through the proposed global and local mean fitting function and variance fitting function,the Gaussian fitting energy function is constructed,and then the weight parameters of the global energy function and the local energy function are dynamically constructed based on the fitting function.The combination of global region information and local region information ensures the effectiveness of the model for image segmentation with intensity inhomogeneity.The experimental results show that the new model overcomes the defects of the traditional local-based model.It not only can effectively segment images with intensity inhomogeneity and strong noise,but also can greatly increase the curve evolution speed and reduce the segmentation time.(2)This paper proposed a new region-based active contour model nemed local intensity clustering image segmentation model with adaptive scale parameters.The segmentation results of the traditional LIC model are seriously affected by the scale parameters.In response to this problem,this paper uses the characteristics of local image entropy to determine the intensity distribution inside the evolution curve,and proposes an adaptively changing scale parameter which increased curve evolution speed,smoothness and accuracy of image segmentation.In addition,the bias field is represented by a linear combination of a given set of smooth polynomial basis function,which ensures the smoothly varying property of the bias field.The numerical experiments and theoretical analysis show that the new model can estimate the bias field while performing image segmentation,and has a good segmentation effect.(3)In order to verify the practicability and effectiveness of the above new models,we applies them to the defect detection of crystalline silicon solar cells,and uses the image segmentation method to detect the defect image.The experimental results show that,for the defect segmentation of solar cell defect images,the proposed models has higher segmentation accuracy and less segmentation time than the C-V model and LIC model.The insensitivity to the initial contour position and good noise resistance are the advantages of the proposed model,Therefore,the models in this paper can better meet the requirements of the image for defect detection and segmentation in terms of segmentation accuracy and time.