Watershed Based Level Set Evolution: A Novel Approach for MRA Segmentation
Jiasheng Hao, Qiang Wang · 2006
Unsupervised segmentation of volumetric data is still a challenging task. Recently, the level set methods have received a great deal of attention, which combine global smoothness with the flexibility of topology changes and offer significant advantages over conventional statistical classification. However, the level set methods suffer from heavy computational burden for a lot of iterations. We present a fast level set framework based on watershed algorithm for the automatic segmentation of complicated structures from volumetric medical images. The driving application is the segmentation of 3-D human cerebrovascular structures from magnetic resonance angiography (MRA), which is known to be a very challenging segmentation problem due to the complexity of vessels geometry and intensity patterns. Experimental results show that the proposed method gives excellent segmentation with fast speed and good accuracy