The Research Of Medical Image Segmentation Methods And Its Application Of Bone Segmentation

Medical image segmentation is the key step from medical image process to analysis. As the main application in clinical medicine, bone segmentation can be used for measurement of tissue volume, make bone treatment and rehabilitation programs and reconstruct 3D image. Accurate results of segmentation can provide reliable information for medical research and clinical diagnostic.This paper analyzed the development status of medical image segmentation technology, primarily discussed three commonly algorithms and their advantages and disadvantages. Such as the deformable model-based method, the region-based method, and the statistical_based method. Using 2D image sequences to generate the volume data can improve higher efficiency and accuracy of segmentation results,played an important role in the process of 3D Visualization. There are several characteristics for CT images : variation range of gray values, noise and the weak edge effects, which bring some difficulties for bone segmentation. It is necessary for us to do some further research on bone segmentation.Compared with segmentation algorithms, this thesis opted the watershed method on brain CT images. To address the problem of over_cut phenomenon, the paper modified the traditional watershed method, including the identification method and the basins merging method in the simulation process of"drown". In addition, this thesis presented a novel method based on histogram entropy in the volum data classification, adjusting the opacity transfer function to complete the boneless of CTA dates. At last, Using Ray_Casting 3D Visualization technology to display the reconstruct result.