Geometric models of human body organs are obtained from imaging techniques like computed tomography (CT) and magnetic resonance image (MRI) that oallow an accurate visualization of the inner body, thus providing relevant information about their its structure and pathologies. Next, these models are used to generate surface and volumetric meshes, which can be used further for visualization, measurement, biomechanical simulation, rapid prototyping and prosthesis design. However, going from geometric models to numerical models is not an easy task, being necessary to apply image-processing techniques to solve the complexity of human tissues and to get more simplified geometric models, thus reducing the complexity of the subsequent numerical analysis. In this work, an integrated and efficient methodology to obtain models of soft tissues like gray and white matter of brain and hard tissues like jaw and spine bones is proposed. The methodology is based on image-processing algorithms chosen according to some characteristics of the tissue: type, intensity profiles and boundaries quality. First, low-quality images are improved by using enhancement algorithms to reduce image noise and to increase structures contrast. Then, hybrid segmentation for tissue identification is applied through a multi-stage approach. Finally, the obtained models are resampled and exported in formats readable by computer aided design (CAD) tools. In CAD environments, this data is used to generate discrete models using finite element methed (FEM) or other numerical methods like the boundary element method (BEM). Results have shown that the proposed methodology is useful and versatile to obtain accurate geometric models that can be used in several clinical cases to obtain relevant quantitative and qualitative information.