Study On Medical Image Segmentation Based On Improved Gradient Vector Flow External Force Active Contour Model

In today’s highly developed information technology,images have become an essential and important method for people to obtain information.In the process of image information processing,image segmentation is often a crucial and undeniable step.Its main purpose is to segment the target image into several meaningful modules,but not overlapping each other,for use in the next step of image analysis or interpretation,The understanding of images directly depends on the final segmentation results.As one of the most widely used methods in image segmentation,Active contour model is more and more widely used in medical image segmentation.In the field of medical image segmentation,active contour method is widely used in segmentation of tumors,blood vessels and various human organ.The active contour method,also known as the "Snake" algorithm,is based on the idea of using one or more curves to adaptively fit the boundaries of the target object.These curves are considered elastic materials,subject to both internal and external forces.Internal forces are usually related to the smoothness of curves,while external forces are calculated from information such as brightness and color of the image.Among them,the gradient vector flow(GVF)is one of the most successful external forces used to attract or push the contour towards the target boundary.Specifically,GVF external forces can be calculated based on various factors such as the grayscale value,gradient value,and region information of the image.By introducing external force gradient vector flow into the energy function,active control of the contour can be achieved,gradually approaching the target boundary.At the same time,the external force of GVF can also control the speed and direction of the contour by adjusting its parameters,further improving the accuracy and efficiency of segmentation.Therefore,GVF external force plays an important role in Active contour model and plays an irreplaceable role in the field of medical image segmentation.Compared to other image segmentation methods,the GVF active contour method can segment the edges of complex shaped target images,and has good robustness to noise and image blur issues.It can also handle the voids and overlapping parts in the image well,and external forces play a crucial role in it,helping the contour automatically follow the target boundary in the image,thus achieving accurate segmentation.Although the GVF active contour method has achieved good results in medical image segmentation,it still has some drawbacks,such as the need to manually set the initial contour,sensitivity to the initial position,and the possibility of leakage when deep concave edges converge.Therefore,in practical applications,it is necessary to improve it or combine it with other method concepts to improve the accuracy and efficiency of image segmentation.In view of these situations,this thesis proposes two improved GVF active contour methods based on GVF Active contour model.Although there are many research thesis on gradient vector flow models both domestically and internationally,there are few studies linking image structure with GVF models,that is,incorporating image structure into GVF models.By rephrasing the smoothness constraints of the GVF model in matrix form,and then incorporating the image structure characterized by the Hessian matrix into the GVF model.In this way,the relevant diffusion partial differential equations are anisotropic,and the Hessian based GVF(HBGVF)active contour has some advantages over other GVF based methods,such as converging to various concave regions and maintaining weak edges.The processing of medical images is usually quite complex,and many existing models often have unsatisfactory convergence performance when facing extremely narrow and deeply concave image edges.Therefore,in response to this situation,this thesis proposes a GVF model on a manifold coupled with Laplacian operators.The main idea is to couple the two components of the GVF model and image gradients during the generation process by using Laplacian operators on the manifold,Due to the coupling term taking into account more image information during the diffusion process,this method can improve the correlation between the grayscale value and gradient value of the image.This model can still capture edge information in the image well even in the presence of noise,allowing for greater external force during convergence to bring the model contour closer to the extremely narrow and deeply concave edge of the segmentation target.This article mainly studies several classic active contour image segmentation models,analyzes their advantages and disadvantages,and compares them with the proposed model.The relevant experiments and comparison results provided in this article will prove the characteristics of this model.