Considering the importance of studying the movement of certain cardiac structures such as left ventricle and myocardial wall for better medical diagnosis, we propose a method for motion estimation and image segmentation in sequential Computed Tomography images. Two main tasks are tackled. The first one consists of a method to estimate the heart's motion based on a bio-inspired image representation model. Our proposal for optical flow estimation incorporates image structure information extracted from the steered Hermite transform coefficients that is later used as local motion constraints in a differential estimation approach. The second task deals with cardiac structure segmentation in time series of cardiac images based on deformable models. The goal is to extend active shape models (ASM) of 2D objects to the problem of 3D (2D + time) cardiac CT image modeling. The segmentation is achieved by constructing a point distribution model (PDM) that encodes the spatio-temporal variability of a training set. Combination of both motion estimation and image segmentation allows isolating motion in cardiac structures of medical interest such as ventricle walls.