Cardiacvascular disease (CVD) is the single leading cause of death in the world, claiming 17.3 million lives a year according to the World Health Organisation (WHO). The development of magnetic resonance (MR) imaging has provided clinicians and researchers with effective tools to detect, assess and monitor the progress of the disease and treatments. MR imaging produces images with high spatial resolution using noninvasive and non-ionising techniques. However, quantitative analysis of the cardiovascular system from MR images remains challenging. The work presented in this thesis focuses on the utilization of cardiac motion information including motion tracking, quantification of the motion and prediction of clinical variables by incorporating motion information. The first main contributions of the thesis are approaches for sparse and dense motion tracking: a sparse set of key landmarks is detected and tracked. They are used as constraints to perform cardiac dense motion tracking using both 3D tagged and untagged image sequences from short-axis and long-axis MR views simultaneously. In order to improve speed and accuracy of the motion tracking, we also develop an approach to identify and track a sparse set of distinctive landmarks in the presence of relatively large deformations for myocardium motion tracking without applying dense motion tracking. An integrated framework is proposed to combine entropy and SVD-based sparse landmark detection with a MRF-based motion tracking framework. In addition, the regional wall thickness systolic dyssynchrony index (SDI) derived directly from sparse motion tracking provides accurate quantification of LV motion, which agrees well with the clinical measurements. In our last contribution, we successfully used manifold learning as a feature selection approach for a SVM-based classification and regression to analyse 209 cardiac MR image sequences. The SVM-based approaches directly operate on the manifold coordinates of the MR images without requiring any non-rigid registration or segmentation and is hence computationally efficient. We demonstrate that, by considering both inter- and intra-subject variation in the manifold learning, we are able to extract both anatomical and functional information. This can be used to construct powerful and reliable classifiers that are more predictive than global indices such as LV volume and mass. The manifold allows for investigating how much temporal information is needed improve the classification performance. The regression experiments demonstrate that there is a very strong correlation between manifold coordinates and obesity indices.