The Human Brain Mr Images Based On Multi-scale Fuzzy Classification And Visualization Methods

3D reconstruction and visualization from medical images has very important application and deep meanings in clinical diagnostic, surgery planning and simulated emulation, plastic and artificial limb surgery, radiotherapy planning, and teaching in anatomy. The technology of 3D reconstruction includes two main methods: surface rendering and volume rendering.The accurate classification of different materials in the brain images is the basis for reconstruction of three-dimensional brain, but there is no better solution for the classification of the volume data. An effective fuzzy segmentation approach based on multi-scale linking model for brain MRI is proposed in this paper. This approach is based on the fact that the image segmentation results should be optimized simultaneously in different scales. At first, the non-uniformity of gray-scale is corrected after analyzing bias field in brain MRI. Then, a new fuzzy inner-scale constraint based on anisotropic diffusion linking model is introduced, which builds an efficient linkage relationship between the high resolution images and low resolution ones. Meanwhile, this paper develops two new fuzzy distances and then embeds them into the fuzzy clustering algorithm. Moreover, a new multi-resolution framework combining the inner-scale and inter-scale constraints is defined.The experiment demonstrated that this method can separate the brain MR image accurately and effectively in which a large amount of brain MRI data was used. The presented framework is robust to noise images and low contrast ones, such as medical images. Finally an ideal reconstruction from the classification of brain tissues was obtained, and it is proved to be valid.