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Segmentation for MRA Image: An Improved Level-Set Approach

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3 Author(s)
Jiasheng Hao ; Harbin Inst. of Technol., Harbin ; Yi Shen ; Qiang Wang

Unsupervised segmentation of volumetric data is still a challenging task. Recently, 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, level-set methods suffer from heavy computational burden because of a lot of iterations. We present a fast level-set framework based on the watershed algorithm for the segmentation of complicated structures from a volumetric data set. The driving application is the segmentation of 3-D human cerebrovascular structures from magnetic resonance angiography, which is known to be a very challenging segmentation problem due to the complexity of vessel geometry and intensity patterns. Experimental results show that the proposed method gives fast and accurate excellent segmentation.

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Instrumentation and Measurement, IEEE Transactions on  (Volume:56 ,  Issue: 4 )