||In this thesis, we focus on two important medical image processing and analy-sis problems: multimodal image registration and cerebrovascular segmentation. First, for the purpose of improving the robustness of image registration, we propose two novel registration objective functions and a new technique for better optimization performance. The first novel objective function is a combination of the multi-dimensional mutual information and an angle measure on the maximum distance-gradient (MDG) vector field. The MDG vector field is a new spatial fea-ture field designed for registration tasks. It encodes both local edge information and globally defined spatial information related to the intensity difference, the distance, and the direction of a voxel to a certain object boundary point. This objective function then integrates both the magnitude and the orientation infor-mation of the MDG feature into the image registration process. The other novel objective function can be obtained if the statistical joint intensity mappings es-timated from the pre-aligned training image pairs are available. This a priori knowledge can then be used as reference to guide the multi-resolution based reg-istration process, by minimizing a new Kullback-Leibler distance (KLD) based similarity measure between the observed joint intensity distribution and the refer-ence joint intensity distribution. To achieve better optimization, which evidently affects the robustness of a registration process, we propose a general technique based on random transformation disturbance to assist optimization procedures to avoid local optima, and hence to achieve robust optimization results. The technique is based on different properties of the local neighborhood of a global optimum and those of local optima. Second, this thesis presents a statistical segmentation technique for the ex-traction of vasculatures in three-dimensional rotational angiography (3D-RA). This method uses maximum intensity projections (MIP) to improve the accuracy and robustness of threshold estimation using the expectation maximization (EM) algorithm. It is fully automatic and computationally efficient. Finally, encouraging experimental results of the proposed approaches on both synthetic and clinical medical images are presented in the thesis.