Accurate segmentation of cerebellum is important in studying the structural changes in brain and the alert in different neuro-developmental disorders. However, cerebellum has received relatively little attention in the image processing literature, compared with cerebrum segmentation. In fact, cerebellum tissue segmentation is also very challenging due to severe partial volume effect and low contrast. In this study, an ensemble sparse learning is proposed for cerebellum tissue segmentation, where the goal is to segment the tissues in cerebellum into white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The experiment results demonstrate that our proposed method show advantages in cerebellum tissue segmentation.
