Segmentation for MRA image: An improved level set approach

Segmentation of the vascular system from Magnetic Resonance Angiography (MRA) volumetric data is still a challenging problem. Level set evolution methods combine global smoothness with the flexibility of topology changes while region-grow algorithms provide pretty fast classification inside the target regions. We present a novel level set framework combined with region-grow algorithm for the segmentation of complicated structures from volumetric medical images. This framework reduces the computational complexity while remains the accuracy. The results demonstrate the potential of our approach. This framework should also be suitable for other 3-D image segmentation that the region of interest to be segmented has a relatively large size in width, height or both.