Image Segmentation Algorithm And 3D Mesh Generation Model Reconstruction Based On MRI Image Sequence

Medical image segmentation and 3D reconstruction are important in medical image research field. The major goal of this dissertation is to explore an algorithm for medical image segmentation and 3D mesh generation model reconstruction based on MRI images of the brain. We analyzed the backgound and status of above-mentioned algorithms, and based on this, we comprehensively used the threshold segmentation algorithm and mathematical morphology algorithm to realize the segmentation of the brain magnetic resonance image sequence. On the threshold selection ,we compared iterative threshold, the largest category of variance and two-dimensional maximum entropy to seek the best threshold. At the same time we used mathematical morphology algorithm to handle the sequence of the MRI images . Experiment proved that the method is high speed, simple and effective, and it can meet the requirements of separate to the interest areas. It can obtain the satisfactive segmentation result to the humanity vision system characteristic .In the three-dimensional image reconstruction, we focused on a section-level reconstruction. For a given MRI medical image sequences, through the contour track we can obtain the border of the image, and then by combining with sampling method at equal interval and the curvature method, we can obtain the feature nodes. By the shortest diagonal algorithm we can finish the contour tiling of the brain contour sequence of the MRI images, and establish 3D mesh generation model of the brain images.Finally we improved the tracking speed of MC algorithm , and reconstructed realistic models of the head and brain.