Level-Set Evolution with Region Competition: Automatic 3-D Segmentation of Brain Tumors

This paper discusses the development of a new method for the automatic segmentation of anatomical structures from volumetric medical images. Driving application is the segmentation of 3-D tumor structures from magnetic resonance images (MRI), which is known to be a very challenging segmentation problem due to the variability of tumor geometry and intensity patterns. Level set evolution combining global smoothness with the flexibility of topology changes offers significant advantages over conventional statistical classification followed by mathematical morphology. Level set evolution with constant propagation needs to be initialized either completely inside or outside and can leak through weak or missing boundary parts. Replacing the constant propagation term by a signed local statistical force overcomes these limitations and results in a region competition method that converges to a stable solution. Applied to MR images presenting tumors, probabilities for background and tumor regions are calculated from a preand post-contrast difference image and mixture-modelling fit of the histogram. The whole image is used for initialization of the level set evolution to segment the blobby-shaped tumor boundaries. Preliminary results on five cases presenting different tumors with significant shape and intensity variability demonstrate that the new method might become a powerful and efficient tool for the clinic. Validity is demonstrated by comparison with manual expert segmentation.