PDE-based Head Visualization Method With Head Medical Data

Head medical data visualization is a hot research topic in medical image processing, and it is of important application significance in clinical medicine. The Partial Differential Equation (PDE) method has been widely used in geometric modeling, and it converts the geometric modeling issue into a PDE solving problem of boundary values. A principal advantage of the PDE method is that a detailed geometric model can be generated by intuitively manipulating a relatively small set of boundary conditions of the PDE. This thesis focuses on head medical image visualization from the following aspects. (1) We adopt a 3D Chan-Vese soft segmentation model based on which the head medical volume data is segmented. And then we extract the head contours from the segmented head volume data as boundary conditions of the PDE method. The experimental results show that the 3D Chan-Vese soft segmentation model can achieve a high segmentation accuracy, and extracted boundaries are consistent with the true head contours. (2) We propose a head medial image visualization framework based on the Bloor-Wilson PDE method. In the proposed framework, we first reconstruct the head surface model by solving the PDE using the extracted boundary conditions. The experimental results show that the proposed method can reconstruct the head surface model with only small amount of head medical slices. And then, by elaborately introducing a third parameter w to the PDE method, we show how a solid head model can be created using the same boundary conditions, based on which the head volume is subsequently rendered with 3D texture mapping. Moreover, the opacity alpha values of texels of the 3D texture map can be associated with the third parameter w of the parametric PDE through a linear transform, and thus we can obtain a natural and perspective view of the head volume. The experimental results have finally demonstrated the feasibility of the PDE based head visualization framework.