Functional magnetic resonance imaging (fMRI) combines high-resolution magnetic resonance imaging with neural activity and is widely used in medical image shooting and auxiliary diagnosis for its non-invasive, non-radiation, high spatial resolution and simultaneous imaging of function and morphology. This paper firstly introduces the medical image segmentation methods, and then presents two different level set segmentation methods: the edge-based Distance Regularized Level Set Evolution (DRLSE) Model and the region-based Region Scalable Fitting (RSF) Model. Finally, the simulation experiments are carried out on these two methods for comparative analysis. The results indicated that the proposed two kind of level set based methods performed high effectiveness in medical image segmentation.
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