Traditional open interventions have been progressively replaced with minimally invasive techniques. Most notably, direct visual feedback is transitioned into indirect, image-based feedback, leading to the wide use of image-guided interventions (IGIs). One essential process of all IGIs is to align some 3D data with 2D images of patient through a procedure called 3D-2D registration during interventions to provide better guidance and richer information. When the 3D data is unavailable, a realistic 3D patient-speci_c model needs to be constructed from a few 2D images. The dominating methods that use only image intensity have narrow convergence range and are not robust to foreign objects presented in 2D images but not existed in 3D data. Feature-based methods partly addressed these problems, but most of them heavily rely on a set of \best" paired correspondences and requires clean image features. Moreover, the optimization procedures used in both kinds of methods are not e_cient. In this dissertation, two topics have been studied and novel algorithms proposed, namely, contour extraction from X-ray images and feature-based rigid/deformable 3D-2D registration. Inspired by biological and neuropsychological characteristics of primary visual cortex (V1), a contour detector is proposed for simultaneously extracting edges and lines in images. The synergy of V1 neurons is mimicked using phase congruency and tensor voting. Evaluations and comparisons showed that the proposed method outperformed several commonly used methods and the results are consistent with human perception. Moreover, the cumbersome \_ne-tuning" of parameter values is not always necessary in the proposed method. An extensible feature-based 3D-2D registration framework is proposed by rigorously formulating the registration as a probability density estimation problem and solving it via a generalized expectation maximization algorithm. It optimizes the transformation directly and treats correspondences as nuisance parameters. This is signi_cantly di_erent from almost all feature-based method in the literature that _rst single out a set of \best" correspondences and then estimate a transformation associated with it. This property makes the proposed algorithm not rely on paired correspondences and thus inherently robust to outliers. The framework can be adapted as a point-based method with the major advantages of 1) independency on paired correspondences, 2) accurate registration using a single image, and 3) robustness to the initialization and a large amount of outliers. Extended to a contour-based method, it di_ers from other contour-based methods mainly in that 1) it does not rely on correspondences and 2) it incorporates gradient information via a statistical model instead of a weighting function. Tuning into model-based deformable registration and surface reconstruction, our method solves the problem using the maximum penalized likelihood estimation. Unlike almost all other methods that handle the registration and deformation separately and optimized them sequentially, our method optimizes them simultaneously. The framework was evaluated in two example clinical applications and a simulation study for point-based, contour-based and surface reconstruction, respectively. Experiments showed its sub-degree and sub-millimeter registration accuracy and superiority to the state-of-the-art methods. It is expected that our algorithms, when thoroughly validated, can be used as valuable tools for image-guided interventions. / published_or_final_version / Orthopaedics and Traumatology / Doctoral / Doctor of Philosophy