Study On CT Image Based Liver Segmentation And Finite Element Simulation Of Puncture

Research related to virtual surgery has never stopped.The virtual surgery system is based on medical images and uses graphics-related technologies to reconstruct human organ models to simulate the virtual surgery environment.CT image as the current one of the most common image in the field of medical image data,the model of soft tissue operation of the simulation training objects,and biopsy tissue as a critical step in virtual surgery,its effect depends on the true extent of soft tissue,that is,the precision of the soft tissue model is a virtual surgery system can reflect the actual mechanical behavior of determinant of human tissues and organs.This paper aims to build a finite element model of acupuncture liver close to the real puncture experiment,based on the mechanical properties of medical CT images and soft tissues,combining medical image processing and finite element analysis,starting from the modeling process,and focusing on image preprocessing.The three aspects of image segmentation and the construction of the needle puncture mechanics model are studied.The specific contents are as follows:Firstly,the basic knowledge of CT images and the commonly used filtering algorithms for preprocessing are introduced in detail.Aiming at the problems of low computational efficiency and image detail loss existing in the adaptive median filtering(AMF)which has better noise reduction effect,the algorithm is improved and improved.In this paper,an improved fast AMF algorithm is proposed.The size of the working window is quickly confirmed to reduce unnecessary computation.Judgment operation is added to the extreme point to prevent it from being removed as noise point and improve the ability of protecting image details.The sorting filter outputs the target pixel points to further improve the efficiency of the algorithm.The experimental results show that,under the same noise density,the improved fast AMF algorithm greatly reduces the noise reduction time of CT images,and the effect and detail retention ability are improved.Secondly,an improved algorithm for CT image segmentation is proposed based on the traditional Canny edge detection algorithm.Firstly,the fast AMF algorithm combined with bilateral filtering instead of Gaussian filtering is used to de-noise the image to eliminate the mixed noise interference.Then the 8-domain template is used to calculate the gradient amplitude so that the edge information in multiple directions can be detected.Finally,the maximum entropy method combined with Ostu method is used to determine the high and low thresholds respectively to reduce the generation of false edges.Through experimental verification,the results prove that the accuracy of the segmentation algorithm in this paper has been improved,and a better segmentation effect has been achieved.Finally,the good image segmentation for 3d reconstruction for the geometric model of the liver,and to optimize smoothing and imported into ANSYS,through the analysis of the super elasticity of soft tissue,the mechanical characteristics of viscoelastic and related materials,put forward a new composite model-strong viscoelastic model,and the constitutive equation of the model is deduced and the fitting get related parameters,so as to build the needle puncture of biomechanics of the finite element model of the liver.Simulation experiments show that,compared with a single model,the proposed model is more consistent with the real mechanical curve of soft tissue and has higher accuracy.