Research On MRI Brain Tumor Segmentation Algorithm Based On Active Contour Model

In recent years,the incidence of brain tumors is increasing,which has become an important factor threatening people’s lives.MR is an important way to diagnose brain tumors.Target recognition and region segmentation of MR brain tumors have become the focus of image research.CV model plays an important role in target recognition and tumor segmentation of brain magnetic resonance images.This paper focuses on brain tumor segmentation algorithm based on improved CV model.The main work is as follows:First of all,The basic principle and evolution process of active contour model are comprehensively studied,and its application in the field of medical image segmentation.A review paper on the research status of active contour models in recent five years was published.The Snake model and CV model are emphatically studied.The advantages and disadvantages of CV model in medical image segmentation are compared and analyzed.Secondly,in order to solve the defects of the CV(Chan-Vese)model algorithm for segmenting MR images with slow gray-scale changes and inconspicuous edge changes,an improved MR image segmentation algorithm based on CV model is proposed.The energy function of CV model is improved,and a new G(R)function is used instead of Dirac function.The parameters of CV model algorithm are optimized,and the accuracy and speed of image segmentation of CV model algorithm are improved.First,a new local term is introduced.Partial histogram equalization preprocessing is used to obtain I’.Let I subtract the original image to obtain▽I.And ▽I is introduced as a local term of the energy function of the CV model.Then,▽I constructs an edge indicating function.Replacing the Dirac function with the newly constructed edge indicating function g(R)solves the problem that the CV model evolution curve cannot detect the edge away from the target.Finally,optimize the smoothing term parameters to reduce the number of iterations and improve operational efficiency.Experiments show that the algorithm has a good segmentation effect on MR images of recurrent glioblastoma in the brain.Finally,In this paper,a preprocessing algorithm is proposed to solve the effect of brain region outside the brain parenchyma on segmentation of MR images in CV model,and to achieve brain tumors in segmented MR images.First,an iterative threshold segmentation algorithm is used for the original image.A region outside the brain parenchyma will form a separate connected domain,and removing the connected domain will result in a binary image of the parenchymal region of the brain.Then,the obtained image is processed by the operation of image hole filling and image erode.In order to improve the contrast betwealgorithm is used to process MR images containing only the brain parenchymal regions.Finally,en brain tumors and surrounding tissues,the FCM CV model algorithm is used for MR images.The second largest closed area enclosed by the active contour,which is the MR image brain tumor areaThe final experiment shows that the method is effective.The accuracy and efficiency of the two improved algorithms presented in this paper have been improved.Considering more changes in the morphology of tumors,further research will be carried out in the future.lt is hoped that the development of computer technology can assist the rapid diagnosis of diseases and improve the precise location of brain tumors.