Abstract:
Brain tumor image analysis has become a major field of research with many valuable clinical applications. The purpose of this thesis is to investigate brain tumor segmentation, growth analysis and brain tumor classification based on time series of multi-modal magnetic resonance image datasets, making use of computer vision and machine learning. To this end, this thesis presents new methods based on graphical models, random forests, supervoxel segmentation and efficient local texture descriptors.