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3D medical image reconstruction refers to a sequence of two-dimensional tomographic data obtained from CT, MRI and other medical imaging equipment is converted into a three-dimensional data , an intuitive three-dimensional reconstruction images show the three-dimensional shape of the human body tissues and organs . This technology has important value in surgery, telemedicine , radiation therapy , virtual endoscopy , medical teaching , therefore, has become a research hotspot . Medical image segmentation is a key step before 3D medical image reconstruction , medical image processing difficulty . In this paper, on the basis of the existing segmentation methods , region growing, full interactive live wire , Fast Marching four kinds of segmentation methods . 3D medical image reconstruction methods are generally sub- surface rendering and volume rendering . This article opposite rendering and volume rendering has conducted in-depth research . Surface rendering , surface rendering of the classic algorithms - the principle of the Marching Cubes algorithm , shortcomings and ways to improve it . In order to improve the the surface rendering real-time interactive capabilities , the paper analyzes the quadric error metric mesh simplification algorithm , experiments show that premise without reducing image quality to improve rendering efficiency . In volume rendering , the paper analyzes the shear deformation of the ray tracing volume rendering algorithm , and a light jump , supplemented by a variety of strategies to improve the ray tracing algorithm , raycasting based 3D texture mapping improved light projected four volume rendering algorithms. Experiments show that : the improved ray tracing algorithm can improve rendering speed in the premise does not change the quality of the reconstructed image . In this paper, the design of 3D medical image reconstruction system MedicalMT includes five modules : image analysis , medical image pre-processing , three-dimensional image processing , interoperability , image processing . The system is successfully used in glioma surface area to volume , brain tumor segmentation reconstruction vessel extraction , extracted bone , brain aneurysm detection , has been recognized by clinicians , the system has a certain value .
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