Research And Application Of Medical Image Processing And Analysis System Based On Virtual Reality

In the past two decades,medical imaging has become one of the fastest growing technologies in the medical field.Because of the birth of medical imaging,doctors' observation of the human tissue structure from impossible to possible.With the continuous development of medical imaging technology,it makes clearer and more direct observation of the lesion possible,which leads the accuracy of diagnostics continues to increase.As the foundation of the development of medical imaging,medical image processing leads medical technology to a more mature application.This thesis is about the medical image processing and analysis system based on VR.It focuses on the most advanced virtual reality technology and researches related technologies in medical image processing,including image pre-processing,image segmentation and 3D reconstruction.Based on this reconstruction technology,a medical image processing and analysis system based on VR is designed and implemented,and a more in-depth study of the core application of the system,lesion area identification.The detailed research includes the following three aspects:Firstly,this thesis studies related technologies of medical image processing,including image preprocessing,image segmentation and 3D reconstruction.Based on this,the principle of virtual reality technology and related implementation approaches are studied.Secondly,according to the characteristics of bladder tumors and real-time goals,the neural network algorithm and the support vector machine algorithm,which are widely used in lesion identification are studied.For their respective defects,a lesion recognition algorithm based on LS-SVM was proposed and verified by experiments.Finally,combining the real-time goals and the research of medical image processing and virtual reality,the design of the medical image processing and analysis system based on VR is completed,the key module VR imaging and VR interaction are realized,and the lesion area identification module,which is the core application of this system has been studied and exemplified.