Diffusion imaging captures the movement of water molecules in tissue by applying varying gradient fields in a magnetic resonance imaging (MRI)-based setting. It poses a crucial contribution to in vivo examinations of neuronal connections: The local diffusion profile enables inference of the position and orientation of fiber pathways. Diffusion imaging is a significant technique for fundamental neuroscience, in which pathways connecting cortical activation zones are examined, and for neurosurgical planning, where fiber reconstructions are considered as intervention related risk structures. Diffusion tensor imaging (DTI) is currently applied in clinical environments in order to model the MRI signal due to its fast acquisition and reconstruction time. However, the inability of DTI to model complex intra-voxel diffusion distributions gave rise to an advanced reconstruction scheme which is known as high angular resolution diffusion imaging (HARDI). HARDI received increasing interest in neuroscience due to its potential to provide a more accurate view of pathway configurations in the human brain. In order to fully exploit the advantages of HARDI over DTI, advanced fiber reconstructions and visualizations are required. This work presents novel approaches contributing to current research in the field of diffusion image processing and visualization. Diffusion classification, tractography, and visualizations approaches were designed to enable a meaningful exploration of neuronal connections as well as their constitution. Furthermore, an interactive neurosurgical planning tool with consideration of neuronal pathways was developed. The research results in this work provide an enhanced and task-related insight into neuronal connections for neuroscientists as well as neurosurgeons and contribute to the implementation of HARDI in clinical environments.