The brain is the most complex part of the human body. Brain tissue is divided into two types: gray matter and white matter. The aim of this study is to describe a dynamic and automatic segmentation method for the gray and white matter. A new variational level set method without re-initialization is the efficient segmentation method which is presented in this thesis. The main research contents of gray and white matter from medical images include image pre-processing, automatic segmentation, 3D reconstruction of brain, and 3D vision of total brain. Morphological filtering and improved Otsu threshold method are used for the image pre-processing. Then the gray and white matter images without noises can be obtained. A new variational level set method without re-initialization for image segmentation is the main research objective in this thesis. It consists of an internal energy term that penalizes deviations of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image feature. This algorithm can be easily implemented using a simple finite difference scheme. Meanwhile, not only can the initial contour can be shown anywhere in the image, but the interior contours can also be automatically detected. Marching cubes (MC) algorithm is a classical algorithm to extract iso-surface from regular volume data. In order to analyze section of the reconstructed tissues and observe the size and structure of inner tissues in multi-tissue reconstruction and visualization, 3D reconstruction of brain and 3D vision of total brain are presented. The thesis provides the visualization and quantification of gray and white matter for the brain disease diagnosis.