Discovery of multiple modalities of Magnetic Resonance Imaging (MRI) has led to challenges regarding how to best utilize and interpret combined information for research and clinical purposes. For example, a MRI study of the brain may involve structural, spectroscopy, perfusion, and functional MRI in different sessions, providing anatomical, metabolic, physiological and functional information. Such data often needs to be analyzed and visualized separately as topological information in each dataset tends to be independent of the other. In this project, we explore a global approach that involves visualization and inter/intra-modality registration support for various MRI modalities and supplement it with a library of optimized tools for 3D image processing, performing multi-step complex DTI computation, gray-white matter classification, cortical thickness calculation, noise removal and perfusion ratio calculation. By quantitative comparisons of reproducibility of results at various processing steps, we measure the variability of the implemented algorithms and also attempt to solve issues like memory management, low robustness and scalability and high processing times.